3679_SaiVivek_FinalHack_Data Prep_Model Building.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CSE 9099o Final Hackathon Batch 81 A _Robust Yield Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Name :** Sai Vivek Kanukuntla    **Enrollment_Id :** 3679 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning and Preprocessing -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import all the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Necessary Imports\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the Working Directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/home/datasets/lab/CSE9099_FinalHack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['train_data_withtarget.csv',\n",
       " 'test_weather.csv',\n",
       " 'train_weather.csv',\n",
       " 'traindata_without_target.csv',\n",
       " 'sample_submission.csv',\n",
       " 'test_data.csv',\n",
       " 'farm_data.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('/home/datasets/lab/CSE9099_FinalHack')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the Datasets from the given path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train_data_withtarget.csv')\n",
    "train_weather = pd.read_csv('train_weather.csv')\n",
    "farm_data = pd.read_csv('farm_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                object\n",
       "farm_id             object\n",
       "ingredient_type     object\n",
       "yield              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                  8784\n",
       "farm_id               1434\n",
       "ingredient_type          4\n",
       "yield              1688175\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>farm_id</th>\n",
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       "      <th>yield</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>fid_110884</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>fid_90053</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>fid_17537</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>fid_110392</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>fid_62402</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date     farm_id ingredient_type  yield\n",
       "0  2016-01-01 00:00:00  fid_110884           ing_w    0.0\n",
       "1  2016-01-01 00:00:00   fid_90053           ing_w    0.0\n",
       "2  2016-01-01 00:00:00   fid_17537           ing_w    0.0\n",
       "3  2016-01-01 00:00:00  fid_110392           ing_w    0.0\n",
       "4  2016-01-01 00:00:00   fid_62402           ing_w    0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>date</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20216095</th>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>fid_41664</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>8.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20216096</th>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>fid_14529</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>4.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20216097</th>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>fid_46454</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20216098</th>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>fid_24103</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>159.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20216099</th>\n",
       "      <td>2016-12-31 23:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>2.850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         date    farm_id ingredient_type    yield\n",
       "20216095  2016-12-31 23:00:00  fid_41664           ing_w    8.750\n",
       "20216096  2016-12-31 23:00:00  fid_14529           ing_w    4.825\n",
       "20216097  2016-12-31 23:00:00  fid_46454           ing_w    0.000\n",
       "20216098  2016-12-31 23:00:00  fid_24103           ing_w  159.575\n",
       "20216099  2016-12-31 23:00:00  fid_34139           ing_w    2.850"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>farm_id</th>\n",
       "      <th>operations_commencing_year</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fid_110884</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fid_90053</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>252.696160</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fid_17537</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>499.446528</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fid_110392</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2200.407555</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fid_62402</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10833.140121</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      farm_id  operations_commencing_year  num_processing_plants  \\\n",
       "0  fid_110884                      2008.0                    NaN   \n",
       "1   fid_90053                      2004.0                    NaN   \n",
       "2   fid_17537                      1991.0                    NaN   \n",
       "3  fid_110392                      2002.0                    NaN   \n",
       "4   fid_62402                      1975.0                    NaN   \n",
       "\n",
       "      farm_area farming_company deidentified_location  \n",
       "0    690.455096     Obery Farms         location 7369  \n",
       "1    252.696160     Obery Farms         location 7369  \n",
       "2    499.446528     Obery Farms         location 7369  \n",
       "3   2200.407555     Obery Farms         location 7369  \n",
       "4  10833.140121     Obery Farms         location 7369  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "farm_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "farm_id                        object\n",
       "operations_commencing_year    float64\n",
       "num_processing_plants         float64\n",
       "farm_area                     float64\n",
       "farming_company                object\n",
       "deidentified_location          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "farm_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>cloudiness</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>22.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>21.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp deidentified_location  temp_obs  cloudiness  \\\n",
       "0  2016-01-01 00:00:00         location 7369      25.0         6.0   \n",
       "1  2016-01-01 01:00:00         location 7369      24.4         NaN   \n",
       "2  2016-01-01 02:00:00         location 7369      22.8         2.0   \n",
       "3  2016-01-01 03:00:00         location 7369      21.1         2.0   \n",
       "4  2016-01-01 04:00:00         location 7369      20.0         2.0   \n",
       "\n",
       "   wind_direction  dew_temp  pressure_sea_level  precipitation  wind_speed  \n",
       "0             0.0      20.0              1019.7            NaN         0.0  \n",
       "1            70.0      21.1              1020.2           -1.0         1.5  \n",
       "2             0.0      21.1              1020.2            0.0         0.0  \n",
       "3             0.0      20.6              1020.1            0.0         0.0  \n",
       "4           250.0      20.0              1020.0           -1.0         2.6  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                 object\n",
       "deidentified_location     object\n",
       "temp_obs                 float64\n",
       "cloudiness               float64\n",
       "wind_direction           float64\n",
       "dew_temp                 float64\n",
       "pressure_sea_level       float64\n",
       "precipitation            float64\n",
       "wind_speed               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_weather.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizations with the Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zuHz5smjfvr1QKpUiMDBQOnY5OTni559/Fu3atZPOp0fv6ZW5fv26dP9YvHhxqXvvvXv3REREhJg1a1aZ5T7//HOhVCpFjx49xK5du4RarRZCFN93tmzZIjp27CiUSqXYtWtXqeW038UePXroHKMpYaJvZKGhoeLLL78Ue/bsEcnJydKNwdCJfmX27NkjJYQ5OTl6rUN7sWjfvr3w8vKq9J+rq6vsRP/VV18VSqVS7NmzR+eYdEn0Y2JipAvgH3/8UWZ+amqqeOmll4RSqRTffPNNqXnLli0TSqVSdOnSpdybiva4VpXoDxs2TPrhIpc2/vIuUNr9VyqV4pdffikzPywsTJrfp08f6YapVVRUJHx8fPT6ATh69Ghp3x5NfIUQYvHixdINMzMzs9Q8QyT6Qghx6dIl6Sbk7e0tlEqlGDlypNBoNOWWr+xmqL05L1iwoNxl8/LyxIABA8r97laU6GuPf/v27UViYmKZdZY8Nw2d6GdnZ4tXXnlFKJVK4efnV2peyURQm3g+Kj09Xbi5uYl27dqJ06dPl1vm5MmTQqVSiZdeeklKHISo/n5XlAht2LBBSrgvXrxYbkzlqU6if+PGDSkh2bx5c5nlsrOzRe/evYVSqRTTp08vNU+bXGh/PJZHm2QFBwfrvD9C6Jboe3l5lfsD/ocffhBKpVL07NlT1jaFkJcsK5VKERMTU2b+2bNnpfk3btwoNW/JkiVCqVSKd999t8LvcWBgoFAqleL77783auyHDx+WtX4tjUYjxowZU+F1uapEX6lUipUrV5ZZLjc3V3Tu3Lnc67Uuib4+n8fHH38sXUNKfse11q5dKy0rJ9HX3jt9fX11Xubq1auiTZs2wsPDQ6qwqmi9/fv3LzX9357os42+kQ0dOhQff/wx+vXrh2effbZGYtixYwcAoH///tV+TFVQUIB79+5V+k/bzEUO7eM/uW1Hq6IdicDFxQUvv/xymfnOzs4YNmwYAGDfvn2l5kVFRQEA3njjjXKHY+vXrx+aNWtWZQxjx47Ve3hDDw8PODo64ubNm7hz5065ZZ5++uly21aXfGw8bty4Mk1/zMzMpNFaEhISdI7p4cOHUvvSCRMmlGmKBACBgYGwtrZGdnY2/vjjD53XLUfr1q0xadIkAMVNOurUqYPPP/8cCoVC1npyc3Ol8+Ttt98ut4yVlRX69u0LoLg5ly60/RT69OlTbnMfDw8PvPTSS7JirUpWVhbi4uIwduxY3Lp1CwAqHE3mtddeg7Ozc7nzIiMjkZ2djY4dO+KFF14ot4ybmxuaNWuG9PR0nD9/vtSygOH3e+fOnQCKmx+2adNG9vL6iIqKgkajQYMGDcr0+QAAGxsbqdlbVFRUhf1pJkyYUO70Xr16AfjfSFSG9MYbb5Q7jLG2idGNGzek/gzG4OHhAQ8PjzLTXVxc8PTTTwMo7v9UUnh4OABgzJgxFX6P+/fvD0D376E+VCoVunXrpteyCoUC3bt3BwCcOHFC9vLW1tZ46623yp2ujUnO9VpL7ueh0Wikd9SMHj0aVlZWZZYdNWoUbG1tZceivd9nZmYiJydHp2XCw8Oh0WjQu3fvCu+7ffr0gZWVFS5dumTwXOJJxjb6tVR2djY2btyIiIgIXL16FYWFhWjWrBl8fX0xduxYnUcsuX37tnRBfP3116sdl6enJ9avX19pmZkzZyIsLEzWel9++WWcPn0aixYtQnJyMvr27Qs3N7dq/zDRtssvOfzgozp37ow1a9YgKSkJ2dnZsLW1RX5+vnTRq6wtqLu7O65fv15pDLoMIRkREYFff/0VFy5cQFpaWrk/lu7evVvuKDKtW7cu94fEU089Jf39/PPPl7tdbRk5HUMvXrwIIQQUCkWFCZuDgwPat2+PEydO4Pz589LN2dACAwPx888/Iy0tDcOGDUPz5s1lr+PcuXMoKCgAUJwcVSQ3NxcAdO4MqD33KktqX3rpJcTExOgaarm0Hc3KM3z4cLz55pvlziuvPa7WyZMnAQBnzpxB165dKyyXnp4OoPiYaNdnjP0uKCiQfkxok6jHQbtNd3f3cn/QAsXXD6D4mn316lW0bt261HwnJ6cKExPt99kYHbNdXV0r3SZQnGjpk6hVZ/vaGFJSUqTzByg+h7SDB3zwwQcVVo5ov6tyO+XKocs1OyUlBevXr8exY8dw7do1ZGVlSe3YtfRJNlu3bl3hZ1Kd80Xu53H9+nWo1WoAFd8DbWxs0L59e9nXsBdffBFOTk5ITU3Fm2++iZEjR8LLy6vSijPtNSkiIgJ//vlnheUKCwsBFH8+DRs2lBWXqWKiXwulpKRg3LhxuHz5MurXr48OHTrAysoKZ8+exYoVKxAVFYX169fr1JHql19+gUajgUqlqvSLXtPGjx+P8+fP4+DBg9i0aRM2bdoECwsLuLq6onfv3hXWqldF2xmwsmEWtfOEEHjw4AFsbW2Rnp4uXbQre4mOLheSRzv/lFRYWIgPP/xQenoAFNcel+wwlpaWBo1GU2HNR0XxlUxMqiqjvTjqQntMHRwcKv3Bqa0levDggc7rlsvCwkJ68Yy+ncJK3oy1HfYqo034q6I9TpWdI4YY/rPkC7MsLCxQr149tGvXDgMHDqy0s3hl56W2U3xubq5O+1uyjDH2Oz09XTpHmzRpImvZ6tDl+qE9z0uWL6my74i1tTUAed8/XVW0Xe02gf8lzcYgd79Ljn5W3nF8lK7fQ31U9t0AikcMmzBhQqknIg4ODtJ+aQcc0OeJibHOF7nrLXndruy7rE8yXbduXXz11VeYNm0a4uPj8d///hdA8X2qa9euGDJkSJlrl/b80A7CURVdnxT8GzDRr2WEEPjwww9x+fJljBo1CtOmTZMSmdzcXMydOxe//vorFi5ciKCgoCrXpa1ZN0RtvjFZWVlh1apVOHXqFKKiohAbG4vz58/j5MmTOHnyJH744Qf8+OOPej+yz8/Pl1Ve6DHkXUUqqgkEgNDQUERFRcHGxgZTpkxBnz59SiUOQHENZkpKikFjMgS5x7S20h7XunXr6jRylTG2XR2PvjBLV5U1J9P+yB0zZgxmzZqld2wVkbvfNX3uV3auy20qRuUrWRseFxen9w93Q6jsml1QUIDp06cjOzsbXl5emDRpElxdXUv9gNq2bRvmzJnzOEI1GmN/57p37y691O/o0aOIi4vD3bt3ER4ejvDwcLzxxhulRjbSnh+zZ8+u9CkmlcU2+rXMn3/+iZMnT8LNzQ2zZ88u9ZrsOnXqYP78+Xjqqaewa9euUo/ZynP8+HFcv34dVlZW8Pf3N3boBuHm5obp06dj69atiI6OxpIlS9CkSROkpaXpNQSdtmZG2165PNq27wqFQmrT6uTkJCVClY2zX9UY/FXRtg2fOHEiRo8eXSbJLyoqMmqNuD60xzQ3N7fSmjftY/jy2gnXJtrmS+np6dX+PEvSHqfKHt8bcnuGpG27/2gbal0YY7+dnJykPibaYRcfB12uHyWbkFRVE0wVK9nUUJ/z7nE5deoUUlJS4OTkhJUrV8LDw6NUkg8UvxPmSVfyXDbWNczBwQFvvPEGvvnmGxw+fBh79uyRmk+Ghobi999/l8pW55r0b8dEv5bRtj3r06dPuTVutra2cHFxQWFhYZUvudi+fTuA4g5ftT3ZKo+trS369++PTz/9FEBxe9mSj+y0NWmV1Ty0a9cOABATE1Nhub///htA8fjs2raRVlZWUlvbuLi4Ctdf2TxdaH9ktG3bttz5J06c0KtzszG1bdtWOvbaY/eozMxMqX1z+/btH1ts+nBxcZGSyP379xtsvdpzLzY2tsIy1W2fbyzaNsoxMTGyf2gaY78tLS2l80hu525drhMV0W7z9OnTFTYF0H4HbG1t0bJlS9nbeJJU51hWpVmzZlIyZ8jvoZahYtdWYLRo0aJURVxJxuwo/Lg0a9ZMeqpS0X0uNzcX586dM9g2W7dujQULFkjXn5JPWLXTDh06JLvJmTaXqukngzWFiX4to+3Y+dVXX5V5g5v2n/ZGV1ltamZmptTuu7Y32wEqfzSu7ZArhCj1BddehCrrmKR9KdmlS5fw22+/lZl/7949bNmyBQDw6quvlpqnHZ1i27ZtyMzMLLNsZGQkrl27VuG2daHdh/JGUSgsLMQ333xTrfUbg5OTk9RU5Pvvvy/TAQ0AvvvuO+Tl5cHW1vaxdp7Uh729vfQW2VWrVlXaTr+wsLDUW2Yroz339u/fX+6bMk+cOFFrE31fX1/Y2toiLy8PX331VaVlH32yaKz91o4sFRYWhn/++Ufn5UqO8CGXj48PzMzM8PDhQ2zdurXM/JycHPzwww9S2cqafJiC6hxLXQwaNAgAsHnzZiQmJlZYTgghOwZDxa59OWNSUlK5lTBHjhzR6S3ftZ2ZmZk0KtTPP/9cbnK9adMmvfohVNXsU/uEpGS5QYMGwczMDHfv3sWaNWsqXf7Ra5Kxz9vajol+LaMdns3T0xODBg2q9F9lndJ2796N3NxcNGnSRO+38z1O/v7+WLJkCc6cOSN9uYUQOHPmjNROz9XVtVQHZO1IMidOnKjwleMeHh7w9vYGAHzyySeIiIiQjvG5c+cwduxYpKenw9nZuUy7v4CAADg5OeHevXsIDAyUhsArLCzEnj17MGvWLL06CJekHdFk5cqVOHDggBRbYmIi/vOf/+DMmTNGGxWjOrSjYpw/fx4fffSRVMulfXX92rVrARR3sq7Jtra6mjp1qjQKxLBhwxAVFVXqJpOcnIyQkBD069dP5xqsfv36oXXr1sjPz8f48eOlGm6NRoPff/8dkydPrrXHpl69epgyZQqA4g79H3zwQakfo3l5eYiNjcX8+fPLvOXWWPs9dOhQtG3bFvn5+RgzZgzCw8OlWvbc3FycOXMGc+bMwenTp0stp30yFx4eXuHwlxVp2rSp1JRg8eLF2Lp1q3ReXL16FePHj0dycjJsbGzw7rvvyt6nJ011jqUuxo8fj2bNmiE7OxujRo1CWFhYqR/Wt2/fRmhoKAYPHlxqAANdGCr2jh07wsbGBg8fPsTHH38sNWvJzc3F9u3bMXnyZDg5Oem9/tpkwoQJsLS0REJCAiZPniw1m8vLy8PGjRuxZMkSve6Bmzdvxrhx47Br165SzYIyMjKwevVqqSZfe+8GgOeee04adjQ4OBjz588vNeJdVlYW/vrrL0yfPh0ffPBBqe01b94clpaWyMzMlIb//TdhZ9xapnHjxgCKa8VGjhyp93q0Y+cPHjxY7zHcH6f79+9jzZo1WLNmDczNzeHg4ICsrCypFqFevXr47LPPSi3j6emJZ599FteuXYOvry/q1asnPUrdtGmT1N79q6++wtixY3Hx4kV88MEHsLa2hoWFhXQDqVu3LlasWFGmeVP9+vWxaNEiTJw4ESdPnoSfnx8cHByQl5eH/Px8dOzYER4eHli7dm25YwzrYuzYsdi3bx+uXbuGSZMmwdLSEtbW1lCr1TA3N8dnn32GFStWGHW8a3107NgR//d//4f58+cjIiICkZGRcHR0hFqtlm6i/v7+GD9+fA1HqptnnnkG3333HSZNmoTr16/jvffeg4WFBezt7ZGdnV0q6de186WlpSWWLVuGgIAAJCcnY+TIkbC1tYVGo0Fubi6aN2+Od955p8pO9TUlICAAmZmZWL58OSIiIhAREQEbGxtYWVkhMzNTepLTtGnTUssZa7+1HfbHjx+PhIQEzJgxA5988gns7e2RkZEhPZZ/tD/S0KFDcfLkSfz000/YsmULnnrqKSgUCvTt2xczZsyocrszZ87E9evX8ddff+G///0vFixYABsbG+lJopWVFRYtWmTyzXaA6h/Lqjg6OuKHH37Au+++i8TERMycOROffPIJHB0dy4wAJbcTtKFid3R0xJQpU/D5559L3wsHBwfk5OSgsLAQbdu2xZAhQ8rcr55Ezz33HObPn4/Zs2fj0KFDOHToEOrWrYvs7GwUFBTA19cXderUQXh4uKx7oBACR44cwZEjRwAUN3uzsLAo9XT+zTffLPM0ePr06cjNzcXmzZul0fns7Oxgbm6OzMxM6Rrw6Ig92mbA4eHheP/99+Hg4CD9QPn444+lp5Cmiol+LfPyyy9j27ZtiIiI0I7xNZUAACAASURBVDvRT0hIwNmzZ6FQKDB48GADR2gcK1euxJEjRxATE4Pbt2/j3r17sLS0RKtWrdC9e3eMGTOmVGctoDihCAkJwbJly3D8+HHcu3dPas5Ucpiw+vXrY+vWrdi0aRN2796Nq1evoqCgAC1atED37t3xzjvvVDhEmLe3N3bs2IEVK1bg+PHjyMnJwTPPPIP+/ftj/Pjx+PrrrwH873GuXE5OTti6dSuWL1+OQ4cO4f79+6hTpw46d+6MsWPHwt3dHStWrNBr3cY2bNgwuLq6Yt26dYiOjsaDBw+k8fPfeOONJ+7i+cILL2Dfvn3YvHkzfvvtN1y5cgWZmZmws7ODUqlEly5d0LdvX1nD1LZu3Rrh4eEIDg7G77//jvT0dDRs2BC9e/fGpEmTpBfS1FYTJ05Er169sHHjRhw/fhwpKSnIzs5GgwYNoFKp0LNnT6nZU0nG2u/GjRtjx44d2Lp1K/bt24dLly4hJycHTZo0QcuWLeHr61vmBV9DhgyBRqNBaGgoLl++jNu3b0tD6erCxsYG3333HX755ReEh4cjPj4eOTk5aNq0Kby8vPDOO++gRYsWeu3Pk6a6x1IXzZs3R3h4OLZv346IiAgkJCQgMzMT1tbWUKlU8PT0RN++fSt9v4mxYx89ejQaN26MdevW4eLFiygqKkKrVq3Qt29fvPPOO9i7d6/sddZWQ4YMQfPmzbF69WqcOnUK+fn5eO655zBkyBCMGjUK7733HgB590B/f3/Y2dnh6NGjiI+PR2pqqnRdcXV1xeuvvy41GyrJ3Nwc8+bNg7+/P7Zs2YK4uDikpqaioKAATZo0Qbt27dC7d+9yl50/fz4aNWqE/fv349atW9LTidpWiWYMCvFv7Z1QQwICAhAdHY1ly5aVmwhpNBq8/vrrOH/+PN58801MmTKlzGPA69ev488//6zwh8AXX3yBn376CV27dsW6deuMsh9UbMSIEYiLi8PChQufmB9VRERE1SWEQI8ePXD79m38/PPPeg3zS8bHRN/Izp8/j/nz50v/v3z5MrKystCiRYtS7c1DQ0Olv1NSUhAYGIiEhATY2dmhTZs2ePrpp/HgwQPcunULSUlJcHZ2xl9//VVmewUFBfD29saDBw+wdOlS9OvXz7g7+C928uRJDBs2DGZmZjh48KDU7IqIiMjU7d69G1OnToW9vT2OHDlS4ShEVLPYdMfI1Gp1mc5hACrsPAoUv2lx+/bt2L59O/bt24eEhAScOXMGTk5OaNiwIcaOHQsfH59ylz148CAePHgAJycnadQY0t/WrVvx4MED9OvXD02bNoW5uTmysrIQFRWFhQsXAigerYdJPhERmZrVq1fDzs4OvXv3RqNGjWBmZob09HSEh4djyZIlAIqfbDPJr71Yo09UiaVLl2L16tUAIHUSzsjIkDoitm3bFuvWreOLcoiIyORMmzYNu3btAlDcL87W1rZU53cvLy+sXr26zEvDqPZgjT5RJfr374+8vDxER0fjzp07SE9Ph729PZ577jn4+vpi2LBh0jj/REREpmTEiBGwt7eXOr5mZmaibt26UKlUGDBgAAYOHCi9cJBqJ9boG8GFCxeQl5cHc3Nz/solIiIiIqPJy8tDUVERrK2tpbeTa/FnmBHk5eVBo9FAo9HIflUzEREREZFc5b2tmYm+EZibm0Oj0cDMzOyxvtVUrVYDQK192ybVHjxXSFc8V0hXPFdIVzxXDCs7OxsajQbm5uZl5jHRNwJra2sUFBTA1tYWKpXqsW03Li4OAB7rNunJxHOFdMVzhXTFc4V0xXPFsOLj46FWq8ttLm5WA/EQEREREZGRMdEnIiIiIjJBTPSJiIiIiEwQE30iIiIiIhPERJ+IiIiIyAQx0SciIiIiMkFM9ImIiIiITBATfSIiIiIiE1QrX5hVUFCA2NhY/PHHHzhx4gRu3bqFhw8fol69eujQoQNGjhyJTp066bXuXbt2YfPmzYiPj4dGo0HLli0xZMgQDB8+HGZm/N1DRERERKahVib6MTExePvttwEADRo0QPv27WFjY4PExERERkYiMjISEydOxAcffCBrvfPnz8emTZtgbW2NLl26wMLCAseOHcOnn36KY8eOYdmyZeW+PpiIiIiI6ElTKxN9hUKBvn37YvTo0fDw8Cg1b+/evZg2bRpWrlyJTp06oXPnzjqtMzIyEps2bUKDBg2wYcMGtGjRAgBw7949jB49GlFRUdiwYQPeeustQ+8OEREREdFjVyvbqnTp0gXLly8vk+QDQL9+/TBo0CAAwK+//qrzOtesWQMAmDZtmpTkA4CzszPmzZsHAPjuu++g0Wj0D5yIiIiIqJaolYl+Vdq1awcAuHPnjk7lU1JScP78eVhaWsLX17fMfE9PTzRq1Aipqak4deqUQWN9nNzd3eHu7l7TYRARERFRLfBEJvpJSUkAitvv6+LChQsAgOeffx516tQpt4yrqysA4OLFi9UPkIiIiIiohtXKNvqVSU1NRVhYGACgT58+Oi1z48YNAECTJk0qLNO4ceNSZZ9kLWbuqekQHpukoP41HQIRERFRrfREJfqFhYWYPn06MjMz0aVLF/Ts2VOn5bKzswEANjY2FZaxs7MDAGRlZVU/0P9PrVYjLi7OYOuryr+52c7jPM6mhMeNdMVzhXTFc4V0xXPF+J6opjv/93//h2PHjqFx48b4+uuvdV5OCAGgeDQfIiIiIqJ/gyemRv+zzz7D9u3b0aBBA4SEhOjcPh/4X229tma/PNqafG1ZQ7C3t4dKpTLY+qhi/+anGfrQ1qLwuFFVeK6QrniukK54rhhWfHw81Gp1ufOeiBr9oKAgrF+/HvXr10dISEip4TF10bRpUwDArVu3KiyTkpJSqiwRERER0ZOs1if6X331FX788Uc4OTnhxx9/ROvWrWWvQzsc56VLl5Cbm1tumbNnzwIA2rZtq3+wRERERES1RK1O9BctWoQffvgBdevWxY8//og2bdrotZ7GjRujffv2KCgoQERERJn50dHRSElJQYMGDdChQ4fqhk1EREREVONqbaL/zTff4LvvvoOjoyPWrVsn1cpXZvHixfD19cXixYvLzBs/fjyA4h8PycnJ0vT79+9j/vz5AIDAwECYmdXaQ0JEREREpLNa2Rn3t99+w6pVqwAAzz77LDZs2FBuuVatWkkJPFA8xv7Vq1eRmppapqyvry+GDx+OzZs3w9/fH15eXrCwsMCxY8egVqvRu3dvjBo1yjg7RERERET0mNXKRD89PV36+9y5czh37ly55Tw9PUsl+lWZN28e3N3dsXHjRkRHR0Oj0aBVq1YYMmQIhg8fztp8IiIiIjIZtTLRHzx4MAYPHix7uaCgIAQFBVVaxt/fH/7+/vqGRkRERET0RGAVNhERERGRCWKiT0RERERkgpjoExERERGZICb6REREREQmiIk+EREREZEJYqJPRERERGSCmOgTEREREZkgJvpERERERCaIiT4RERERkQliok9EREREZIKY6BMRERERmSAm+kREREREJoiJPhERERGRCWKiT0RERERkgpjoExERERGZICb6REREREQmiIk+EREREZEJYqJPRERERGSCmOgTEREREZkgJvpERERERCaIiT4RERERkQliok9EREREZIKY6BMRERERmSAm+kREREREJoiJPhERERGRCWKiT0RERERkgpjoExERERGZICb6REREREQmiIk+EREREZEJYqJPRERERGSCmOgTEREREZkgJvpERERERCaIiT4RERERkQliok9EREREZIKY6BMRERERmSAm+kREREREJoiJPhERERGRCWKiT0RERERkgpjoExERERGZICb6REREREQmiIk+EREREZEJYqJPRERERGSCmOgTEREREZkgJvpERERERCaIiT4RERERkQliok9EREREZIKY6BMRERERmSAm+kREREREJoiJPhERERGRCWKiT0RERERkgpjoExERERGZICb6REREREQmiIk+EREREZEJYqJPRERERGSCmOgTEREREZkgJvpERERERCaIiT4RERERkQliok9EREREZIKY6BMRERERmSAm+kREREREJsiipgOoyJUrV3D48GGcPXsW586dQ1JSEoQQWLZsGXx9fWWvb+bMmQgLC6twfsuWLREREVGdkImIiIiIag29E/1jx47h6NGjuH37NvLy8hAcHCzNy83NRVJSEhQKBVQqlV7r37x5M37++Wd9w6tQx44d0bx58zLTGzRoYPBtERERERHVFNmJ/u3bt/HRRx/h9OnTAAAhBBQKRaky5ubmmDhxIlJSUhAeHg6lUik7MKVSiXHjxsHFxQUuLi6YPXs2oqOjZa/nUUOHDsXgwYOrvR4iIiIiotpMVqKvVqsxZswYJCcno3nz5ujZsydCQ0ORnZ1dqpylpSWGDx+OxYsXIzIyUq9Ef+jQobKXISIiIiKiYrI64/70009ITk6Gp6cndu7ciRkzZsDGxqbcsr169QIA/PXXX9WPkoiIiIiIZJFVox8VFQWFQoFPPvkEderUqbRsq1atYGlpiatXr1YrQEM7fvw44uPjkZ2djaeeegru7u7o2rUrzMw4ABERERERmQ5ZiX5ycjKsra3Rpk0bncrb2dlBrVbrFZixhIeHl5nWunVrLFmyRO+Ow0REREREtY3szrhCCJ3KFRQUQK1Ww87OTnZQxtCmTRvMmTMHXbp0QZMmTaBWq3HhwgUsXboU//zzD95++22EhYWhUaNGBtumWq1GXFycwdZXFXd398e2rdrmcR5nU8LjRrriuUK64rlCuuK5Ynyy2qs0adIE+fn5uHHjRpVljx49isLCQrRo0ULf2AxqzJgxCAgIQOvWrWFra4uGDRvilVdewbZt2+Dm5ob79+9jzZo1NR0mEREREZFByKrR9/b2xpUrV7B+/XrMmjWrwnJqtRpfffUVFAoFXnnllerGaFRWVlYYP348Jk6ciD/++MOg67a3t2dzoMfk3/w0Qx/aWhQeN6oKzxXSFc8V0hXPFcOKj4+vsKm8rBr9sWPHwsbGBuvXr8eKFSuQkZFRan52djb27NmDwYMHIzExEfXq1cOoUaP0j/wxadWqFQDgzp07NRwJEREREZFhyKrRb9iwIZYvX47Jkyfj22+/xerVq6U2+z4+Prh9+zaKiooghICtrS2WL18OBwcHowRuSA8fPgSAWtOfgIiIiIioumSPKdmtWzds374dL7/8MoqKiqTE/vr16ygsLIQQAt26dcO2bdvg4eFhjJgNbt++fQAAFxeXGo6EiIiIiMgwZI+6AwDPPfcc1qxZg7S0NJw+fRqpqanQaDRwdnbGiy++iAYNGhg6Tp0sXrwYUVFR8PHxwdSpU6XpFy9eREpKCl5++WWYm5tL0wsLC7F+/XqsX78eQHGHXSIiIiIiU6BXoq9Vv3599OjRw1CxlHL+/HnMnz9f+v/ly5cBAEuXLsW6deuk6aGhodLfqampuHr1KlJTU0ut6+bNm5g0aRKcnJzQokULNGrUCFlZWUhISMDdu3dhZmaGadOmwdvb2yj7QkRERET0uFUr0TcmtVqN06dPl5melJQke10qlQqjR4/G2bNncfPmTVy4cAEKhQJPP/00Bg8ejJEjR7LZDhERERGZFFmJvr5vubW3t5e9TKdOnRAfHy9rmaCgIAQFBZWZ3qxZM8yePVt2DERERERETypZif5LL70kewMKhQIXLlyQvRwREREREelPVqKvHUrT2MsQEREREVH1yEr0Y2JiKp2fmZmJs2fP4ocffkBycjK+/vprdOjQoVoBEhERERGRfLIS/apefuXg4IAmTZrAx8cHEydOxJQpU7Bjx44n4qVZRERERESmRPYLs3RaqZkZZs6cCbVajRUrVhhjE0REREREVAmjJPoA0KJFCzg4OODYsWPG2gQREREREVXAaOPo5+XlITs7G3l5ecbaBBERERERVcBoNfrbt29HUVERGjdubKxNEBERERFRBWTV6P/zzz+Vzs/Pz0dKSgp+++037NmzBwqFAn5+ftUKkIiIiIiI5JOV6A8cOBAKhaLKctqx87t06YIJEyboFxkREREREelNVqJvb29faaJvbm4OR0dHKJVKvPrqq+jXr59OPwyIiIiIiMiwZCX6sbGxxoqDiIiIiIgMyGidcYmIiIiIqOYw0SciIiIiMkFM9ImIiIiITFCFbfQHDRpkkA0oFAr88ssvBlkXERERERHppsJE/+LFiwbZAEfdISIiIiJ6/CpM9FesWPE44yAiIiIiIgOqMNHv3bv344yDiIiIiIgMiJ1xiYiIiIhMEBN9IiIiIiITJOvNuI9KTEzE3bt3kZOTAyFEheV69epVnc0QEREREZFMshP9/Px8rFy5Elu2bEF6enqV5RUKBS5cuKBXcEREREREpB9ZiX5BQQHGjRuH2NhYCCHQpEkT3Lp1CxYWFmjZsiXu37+PtLQ0AICjoyOaNGlilKCJiIiIiKhystroh4aGIiYmBo0bN8auXbtw8OBBAICTkxN27dqFo0ePYufOnejevTuys7Px+uuvIzw83CiBExERERFRxWQl+nv27IFCocDMmTPx/PPPl1tGpVJhzZo16NmzJ7744gscP37cIIESEREREZHuZCX6ly9fBgC88sorpaYXFhaWKTtt2jRoNBqEhIToHRwREREREelHVqKfm5sLR0dHWFlZSdOsra2RnZ1dpuyzzz4LBwcHnDlzpvpREhERERGRLLISfWdnZ6jVahQVFUnTnnrqKRQUFOD27dulyhYVFSEnJwcZGRmGiZSIiIiIiHQmK9Fv2rQpNBoNUlJSpGkuLi4AgH379pUqGxkZicLCQjRs2NAAYRIRERERkRyyhtf09vZGbGwsDh8+jGHDhgEABg4ciP379+Obb75Beno62rRpg4SEBISEhEChUKBPnz5GCZyIiIiIiComK9H38fHB9u3bERcXJyX6PXv2xGuvvYadO3di7dq1UlkhBFQqFSZNmmTYiImIiIiIqEqyEv2WLVti//79ZaZ/+eWX8Pb2RmRkJFJSUuDg4AAvLy+MHDkSNjY2BguWiIiIiIh0IyvRr4yfnx/8/PwMtToiIiIiIqoGWZ1xiYiIiIjoySAr0R8zZgx27NiBzMxMY8VDREREREQGICvR//vvvzFnzhx07doVkydPRkREBPLz840VGxERERER6UlWG/1x48Zh3759uHXrFqKionDgwAHY2dnBx8cH/fv3h5eXF8zM2BqIiIiIiKimycrKp0+fjoMHD2LTpk0YMWIE6tevD7VajbCwMAQGBsLb2xufffYZTp06Zax4iYiIiIhIB3qNutOxY0d07NgRc+bMwdGjR7F7924cOHAA9+/fx8aNG7Fx40Y0bdpUGomndevWho6biIiIiIgqUa3hNc3MzNCtWzd069YN+fn5+P3337F79278+eefuHHjBtasWYO1a9fiwoULhoqXiIiIiIh0YLAG9VZWVujTpw+WL1+OHTt2oF27dhBCQAhhqE0QEREREZGODPbCrIcPHyIyMhK7d+9GXFyclOCzcy4RERER0eNXrUQ/JycHBw4cwJ49e3DkyBEUFRVJCX67du3g5+eH/v37GyRQIiIiIiLSnexEv7CwEH/++Sd2796NQ4cOITc3V0rumzdvjv79+8Pf3x8tW7Y0eLBERERERKQbWYn+3LlzsX//fmRkZEjJfYMGDfDqq6/Cz88PL7zwglGCJCIiIiIieWQl+tu2bQMAODg4wMfHB/7+/ujcuTMUCoVRgiMiIiIiIv3ISvT79OkDf39/dO/eHVZWVsaKiYiIiIiIqklWor98+XJjxUFERERERAbEsS+JiIiIiEwQE30iIiIiIhPERJ+IiIiIyAQx0SciIiIiMkFM9ImIiIiITBATfSIiIiIiE8REn4iIiIjIBMlK9P/55x8kJibqXD4xMRH//POP7KCIiIiIiKh6ZL0wa+DAgWjQoAEOHz6sU/nAwECkpKTgwoULegVHRERERET6kd10Rwhh1PJERERERFR9Rm2jn5OTAwsLWQ8NiIiIiIjIAIyWhSckJODBgwdo0qSJXstfuXIFhw8fxtmzZ3Hu3DkkJSVBCIFly5bB19dX77h27dqFzZs3Iz4+HhqNBi1btsSQIUMwfPhwmJmxbzIRERERmYZKE/29e/di7969paZlZmbivffeq3AZIQQyMjJw7tw5KBQKdO3aVa/ANm/ejJ9//lmvZSsyf/58bNq0CdbW1ujSpQssLCxw7NgxfPrppzh27BiWLVsGc3Nzg26TiIiIiKgmVJroX7lyBQcOHCg1LS8vr8y0irRp0wYffvihXoEplUqMGzcOLi4ucHFxwezZsxEdHa3XugAgMjISmzZtQoMGDbBhwwa0aNECAHDv3j2MHj0aUVFR2LBhA9566y29t0FEREREVFtUmuh7e3vDwcEBQHFNfVBQEOzt7TF58uQKlzEzM4O9vT1at24NV1dXvQMbOnSo3suWZ82aNQCAadOmSUk+ADg7O2PevHkICAjAd999h4CAADbhISIiIqInXqWJ/osvvogXX3xR+n9QUBDq1KnzxNV6p6Sk4Pz587C0tCy3fb+npycaNWqEO3fu4NSpU+jYsWMNRElEREREZDiyqq5jYmIQERFhrFiMRjuO//PPP486deqUW0b79OHixYuPLS4iIiIiImORNeqOthlPSYWFhcjNzYW9vb3BgjK0GzduAEClIwA1bty4VFkiIiIioieZXsNrJiQk4Pvvv8fRo0dx//59KBSKUm+/zcjIwMqVK6FQKDBlyhRYWloaLGB9ZGdnAwBsbGwqLGNnZwcAyMrKMth21Wo14uLiDLa+qri7uz+2bdU2j/M4mxIeN9IVzxXSFc8V0hXPFeOTneiHh4dj7ty5KCgoqLCMo6MjLl++jL/++guenp7o0aNHtYKsLu3beRUKRY3GQURERET0uMhK9P/55x/MmTMHGo0GEyZMgJ+fH8aMGYO0tLQyZV9//XUcOXIE+/fvr/FEX1tbr63ZL4+2Jl9b1hDs7e2hUqkMtj6q2L/5aYY+tLUoPG5UFZ4rpCueK6QrniuGFR8fD7VaXe48WYn+Dz/8gKKiIkycOFEaYrOiWvJOnToBAE6dOiVnE0bRtGlTAMCtW7cqLJOSklKqLBERERHRk0z2qDsAEBAQUGXZevXqwc7OTkqga1K7du0AAJcuXUJubm65Zc6ePQsAaNu27WOLi4iIiIjIWGQl+vfu3YO9vT2cnJx0Km9ubo7CwkK9AjOkxo0bo3379igoKCh3eNDo6GikpKSgQYMG6NChQw1ESERERERkWLISfTs7O2RlZVXaEVcrLS0NmZmZqFevnt7BybV48WL4+vpi8eLFZeaNHz8eALBo0SIkJydL0+/fv4/58+cDAAIDA/lWXCIiIiIyCbLa6CuVSsTGxuLEiRNSG/yKbN++HUII6UVUcp0/f15KwAHg8uXLAIClS5di3bp10vTQ0FDp79TUVFy9ehWpqall1ufr64vhw4dj8+bN8Pf3h5eXFywsLHDs2DGo1Wr07t0bo0aN0itWIiIiIqLaRlaiP2DAAMTExGDp0qUICQmp8C2zBw8eRHBwMBQKBQYOHKhXYGq1GqdPny4zPSkpSa/1AcC8efPg7u6OjRs3Ijo6GhqNBq1atcKQIUMwfPhw1uYTERERkcmQlegPGTIEO3bswKlTpzB06FC88cYbUjOeuLg4XL58Gfv378fRo0chhEDXrl3h4+OjV2CdOnVCfHy8rGWCgoIQFBRUaRl/f3/4+/vrFRMRERER0ZNCVqJvZmaG1atX44MPPsDx48fxxRdfSPO0zV60L6fy8vLCN998Y8BQiYiIiIhIV7LfjOvk5ISQkBBERkYiPDwcJ0+eRHp6OoDiF0S5ublh8ODBePXVV/kmWiIiIiKiGiI70QeKX5Ll6+sLX19fAEBBQQGEELCysjJocEREREREpB+9Ev1HWVpaGmI1RERERERkIBxmhoiIiIjIBMmq0f/5559lrdzKygqOjo5o1aoVVCoV2+wTERERET0mshL9L774Qu9k3dnZGWPGjMHbb7/N8eqJiIiIiIxMVqLfs2dPKBQKxMTEICMjAxYWFnjuuefQsGFDAMVvpr18+TIKCwtRt25duLu7IzMzEwkJCUhNTcWiRYtw+vRpLF++3Cg7Q0RERERExWQl+itXrsSsWbOQmZmJCRMm4O2334aTk1OpMunp6fjxxx+xdu1aODs7Y+XKlSgsLERoaCi++OILREVFYc+ePejfv79Bd4SIiIiIiP5HVhuaLVu2IDw8HFOmTMFHH31UJskHgLp16+LDDz/ERx99hG3btmH79u2wsLDAiBEjMHnyZAghEBYWZrAdICIiIiKismQl+qGhoTA3N5fegluZUaNGwdzcHFu2bJGmDR06FABw8eJFmWESEREREZEcshL9pKQk2NnZwcbGpsqyNjY2sLOzw9WrV6Vp9evXR926dZGRkSE/UiIiIiIi0pmsRN/CwgIZGRm4e/dulWXv3r2LjIyMMiPs5OTkwN7eXl6UREREREQki6xE38XFBQAQFBQEIUSlZb/88ksIIeDq6ipNu3v3LvLz8+Hs7KxHqEREREREpCtZiX5gYCCEENi3bx9GjBiBAwcOIC0tTZqflpaGqKgojBgxAnv37oVCoUBgYKA0/9ChQwCADh06GCh8IiIiIiIqj6zhNbt06YLZs2cjKCgIJ0+exOTJkwFAap6j0WgAAEIImJmZYcaMGejSpYu0/O3bt/HKK6/Az8/PUPETEREREVE5ZCX6ABAQEAA3NzesXLkShw8fRmFhIYqKiv63QgsLeHt7Y+LEiaWa7QDAhx9+WP2IiYiIiIioSrITfQBwdXXFqlWrkJ+fj4SEBDx48ABCCNSvXx9KpRJWVlaGjpOIiIiIiGSQlejHxMQAAFQqFRwdHWFlZSV10CUiIiIizFdm+AAAIABJREFUotpDVqIfEBAAc3NzHDt2zFjxEBERERGRAchK9B0dHaFQKODo6GiseIiIiIiIyABkDa/ZqlUrZGVlITs721jxEBERERGRAchK9IcMGYLCwkKEhoYaKx4iIiIiIjIAWU13hg4ditjYWCxatAgajQYjRoxAnTp1jBUbERERERHpSVai/9577wEArKys8PXXXyM4OBgqlQr16tWDubl5ucsoFAoEBwdXP1IiIiIiItKZrET/wIEDUCgUEEIAAHJycnDq1KlKl1EoFPpHR0REREREepGV6M+aNctYcRARERERkQHJSvTfeustY8VBREREREQGJGvUHSIiIiIiejIw0SciIiIiMkGymu48KjExEXfv3kVOTo7UQbc8vXr1qs5miIiIiIhIJtmJfn5+PlauXIktW7YgPT29yvIKhQIXLlzQKzgiIiIiItKPrES/oKAA48aNQ2xsLIQQaNKkCW7dugULCwu0bNkS9+/fR1paGgDA0dERTZo0MUrQRERERERUOVlt9ENDQxETE4PGjRtj165dOHjwIADAyckJu3btwtGjR7Fz5050794d2dnZeP311xEeHm6UwImIiIiIqGKyEv09e/ZAoVBg5syZeP7558sto1KpsGbNGvTs2RNffPEFjh8/bpBAiYiIiIhId7IS/cuXLwMAXnnllVLTCwsLy5SdNm0aNBoNQkJC9A6OiIiIiIj0IyvRz83NhaOjI6ysrKRp1tbWyM7OLlP22WefhYODA86cOVP9KImIiIiISBZZib6zszPUajWKioqkaU899RQKCgpw+/btUmWLioqQk5ODjIwMw0RKREREREQ6k5XoN23aFBqNBikpKdI0FxcXAMC+fftKlY2MjERhYSEaNmxogDCJiIiIiEgOWcNrent7IzY2FocPH8awYcMAAAMHDsT+/fvxzTffID09HW3atEFCQgJCQkKgUCjQp08fowROREREREQVk5Xo+/j4YPv27YiLi5MS/Z49e+K1117Dzp07sXbtWqmsEAIqlQqTJk0ybMRERERERFQlWYl+y5YtsX///jLTv/zyS3h7eyMyMhIpKSlwcHCAl5cXRo4cCRsbG4MFS0REREREupGV6FfGz88Pfn5+hlodERERERFVQ6WdcUePHo3333//ccVCRP+vvXsPyrLO/z/+uhHBAwIaKOjQV9gCz4dsENzJmo2UdHRM11byUGnYbI0dRtdc80S7m61iHmcbxxJMhHIsTS1h3TyusoIWeYOKknhIxNDwgAdA7/v3h7/73lhAbuAGbi+fjxlm4Lo+1/v6XPiZy9f94ToAAAA4yT1n9DMyMuTn59dYfQEAAADgJLV6vCYAAACA+wNBHwAAADAggj4AAABgQAR9AAAAwIBqfLxmSUmJ/vznP9d5ByaTSe+//36dtwcAAABQezUG/dLSUm3atKlOxa1WK0EfAAAAaAI1Bn13d3f16dOnMfoCAAAAwElqDPo+Pj5au3ZtY/QFAAAAgJNwMy4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZ0z6fuHDt2rLH6AQAAAMCJmNEHAAAADKjG5+g3tS1btiglJUW5ubmyWCwKDg7WqFGjFBMTIzc3xz+nLF++XCtWrKh2vYeHh8xmszO6DAAAADQ5lw76cXFxSk5OlqenpyIjI+Xu7q709HS99957Sk9P19KlS9WsWbNa1ezSpYu6du1aabm7u0v/KgAAAIBacdl0m5aWpuTkZPn7+yspKUmdO3eWJF28eFETJkzQ9u3blZSUpBdffLFWdaOiojRlypQG6DEAAADgOlz2Gv2VK1dKkqZNm2YP+ZLk5+enefPmSZJWrVoli8XSBL0DAAAAXJtLBv3CwkLl5OSoefPmio6OrrQ+PDxcHTp0UFFRkbKyspqghwAAAIBrc8lLd44cOSJJevTRR9WiRYsq2/Ts2VMXLlzQ0aNH9dhjjzlcOycnRwsXLtTVq1fl4+Oj3r1768knn5SHh4dT+g4AAAC4ApcM+j/99JMkqWPHjtW2CQwMrNDWUTt37tTOnTsrLAsICNDChQsVHh5ey57eW0lJiQ4dOuTUmvfSr1+/RtuXq2nM37OR8HuDoxgrcBRjBY5irDQ8l7x058aNG5Kkli1bVtumdevWkqTr1687VDMoKEhTp07VV199pUOHDik9PV1r1qxReHi4CgsLNXnyZF4QBgAAAMNwyRl9q9UqSTKZTE6rOWLEiErLIiIiFBERoTfeeENpaWlavHix/SZgZ/Dy8lJYWJjT6qF6D/JfM+rCNovC7w01YazAUYwVOIqx4ly5ubkqKSmpcp1LzujbZuttM/tVsc3k29rWx2uvvSZJ2rdvn8rLy+tdDwAAAGhqLhn0O3XqJEkqKCiotk1hYWGFtvUREhIiSSovL1dxcXG96wEAAABNzSWDfrdu3SRJJ06c0K1bt6psYzabJanKt9zW1uXLl+3ft2rVqt71AAAAgKbmkkE/MDBQ3bt3V3l5uVJTUyutz8jIUGFhofz9/dW3b99672/btm2SpODgYHl5edW7HgAAANDUXDLoS9LkyZMlSfHx8Tp9+rR9+aVLlxQXFydJio2NlZvbfw8hKSlJ0dHRmj59eoVaBQUF2rJli8rKyiost1qt2rRpkz788ENJ0ksvvdQQhwIAAAA0Opd86o4kRUdHKyYmRikpKRo2bJgGDBggd3d3paenq6SkRFFRURo3blyFbYqLi5Wfny9/f/8Ky69cuaJp06Zp7ty5Cg4OVseOHVVeXq4TJ07Yn8M/btw4jRkzptGODwAAAGhILhv0JWnevHnq16+f1q1bp4yMDFksFoWEhGjUqFGKiYmpMJt/LwEBAZo0aZLMZrPOnDmjvLw8WSwW+fv7a8iQIXr++ecVGRnZwEcDAAAANB6XDvqSNGzYMA0bNsyhtlOmTNGUKVMqLW/btm2ly3kAAAAAI3PZa/QBAAAA1B1BHwAAADAggj4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAggj4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAggj4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAggj4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAggj4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAggj4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAggj4AAABgQAR9AAAAwIAI+gAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAggj4AAABgQO5N3YGabNmyRSkpKcrNzZXFYlFwcLBGjRqlmJgYubnV/nPKnj17lJiYqOzsbJWWliooKEhDhw7VpEmT5OHh0QBHAAAAADQ+lw76cXFxSk5OlqenpyIjI+Xu7q709HS99957Sk9P19KlS9WsWTOH661atUrx8fFq1qyZwsPD5e3trczMTC1ZskS7du1SYmKiWrZs2YBHBAAAADQOlw36aWlpSk5Olr+/v5KSktS5c2dJ0sWLFzVhwgRt375dSUlJevHFFx2qZzabtWjRIrVs2VJr1qxR7969JUnXr1/Xq6++qszMTC1evFgzZ85sqEMCAAAAGo3LXqO/cuVKSdK0adPsIV+S/Pz8NG/ePEl3Z+gtFotD9VatWiWr1apXXnnFHvIlqXXr1po/f77c3NyUnJysq1evOu0YAAAAgKbikkG/sLBQOTk5at68uaKjoyutDw8PV4cOHVRUVKSsrKwa65WVlWnPnj2SpOHDh1daHxQUpD59+qi8vFy7d++u/wEAAAAATcwlg/6RI0ckSY8++qhatGhRZZuePXtKko4ePVpjvfz8fN28eVO+vr56+OGH71nPtm8AAADgfuaSQf+nn36SJHXs2LHaNoGBgRXaOlLPtk1VbPs6d+6cw/0EAAAAXJVL3ox748YNSbrnE3Bat24t6e7NtM6o16pVK4fr1aS0tFSSVFJSokOHDtW7nqO8vLwkSWkvP9Jo+2xqubm5Td2F+5JtrPD7Q00YK3AUYwWOMupYKSkpadL92/Lnr7lk0LdarZIkk8nkkvVqcufOnUbZz/9q6gEGAACAplFV/nTJoG+brbfNxFfFNvNua1vferZ1jtSriaenp0pLS9WsWTN5enrWux4AAABQldLSUt25c6fKzOmSQb9Tp06SpIKCgmrbFBYWVmjrSL3z589X28a2zpF6NenWrVu9awAAAAD14ZI349qC8okTJ3Tr1q0q25jNZklS165da6wXEhKiFi1a6PLlyzpz5kyVbQ4fPuxwPQAAAMDVuWTQDwwMVPfu3VVeXq7U1NRK6zMyMlRYWCh/f3/17du3xnoeHh4aOHCgJGnz5s2V1p89e1ZZWVlq3ry5nnrqqXr3HwAAAGhqLhn0JWny5MmSpPj4eJ0+fdq+/NKlS4qLi5MkxcbGys3tv4eQlJSk6OhoTZ8+vVK92NhYmUwmffzxx/bZe+nutf4zZ86UxWLRCy+8IG9v74Y6JAAAAKDRuOQ1+pIUHR2tmJgYpaSkaNiwYRowYIDc3d2Vnp6ukpISRUVFady4cRW2KS4uVn5+vvz9/SvV69Wrl6ZOnar4+HiNGTNGERERatOmjTIzM3Xp0iX17t1bb7/9dmMdHgAAANCgXDboS9K8efPUr18/rVu3ThkZGbJYLAoJCdGoUaMUExNTYTbfEbGxsQoLC1NCQoLMZrNKS0sVFBSk8ePHa9KkSfLw8GigIwEAAAAal8lqe8g8AAAAAMNw2Wv0AQAAANQdQR8AAAAwIII+AAAAYEAEfQAAAMCACPoAAACAARH0AQAAAAMi6AMAAAAG5NIvzHrQbdmyRSkpKcrNzZXFYlFwcHCdXxYmSXv27FFiYqKys7PtLwsbOnQoLwszAGeNleXLl2vFihXVrvfw8JDZbHZGl9HITp48qb1798psNis7O1unTp2S1WrV0qVLFR0dXee6zj5Poek5e6zMmDFDGzdurHZ9cHCwUlNT69NlNIHy8nIdPHhQu3fv1nfffaeCggJdvnxZbdu2Vd++fTV27Fj179+/TrU5rzgPQd9FxcXFKTk5WZ6enoqMjJS7u7vS09P13nvvKT09XUuXLlWzZs0crrdq1SrFx8erWbNmCg8Pl7e3tzIzM7VkyRLt2rVLiYmJatmyZQMeERqKs8eKJHXp0kVdu3attNzdnVPG/SolJUWffvqpU2s2xNhD02uIsSJJjz32mP7v//6v0nJ/f3+n7wsNLzMzUy+//LKku/+G3bt3V8uWLfXjjz8qLS1NaWlpeu211/Tmm2/Wqi7nFSezwuWkpqZaQ0NDrb/97W+t+fn59uVFRUXWZ5991hoaGmpNTEx0uN7hw4etYWFh1t69e1uzsrLsy0tKSqxjx461hoaGWv/2t7858xDQSJw9VpYtW2YNDQ21Llu2rAF6i6a0fv1669///nfr119/bT19+rR13Lhx1tDQUOu2bdvqVM/ZYw+uw9lj5Z133rGGhoZav/jiCyf3FE1p//791ilTplgzMzMrrfv666+tXbt2tYaGhlrT09Mdrsl5xfn4+4cLWrlypSRp2rRp6ty5s325n5+f5s2bJ+nuDL3FYnGo3qpVq2S1WvXKK6+od+/e9uWtW7fW/Pnz5ebmpuTkZF29etVpx4DG4eyxAuMaPXq0pk+friFDhujhhx+udz3GnnE5e6zAmCIjI7Vs2TI9/vjjldYNGTJEzz33nCRp8+bNDtfkvOJ8BH0XU1hYqJycHDVv3rzKayHDw8PVoUMHFRUVKSsrq8Z6ZWVl2rNnjyRp+PDhldYHBQWpT58+Ki8v1+7du+t/AGg0zh4rgKMYewBq0q1bN0nShQsXHGrPeaVhEPRdzJEjRyRJjz76qFq0aFFlm549e0qSjh49WmO9/Px83bx5U76+vtXOzNjq2faN+4Ozx8qv5eTkaOHChZo9e7bi4+O1fft2lZWV1a/DMIyGHHswrgMHDmj+/PmaPXu2lixZor179zIza2CnTp2S5Pg9GJxXGgZ31rmYn376SZLUsWPHatsEBgZWaOtIPds2VbHt69y5cw73E03P2WPl13bu3KmdO3dWWBYQEKCFCxcqPDy8lj2F0TTk2INxbdq0qdKyRx55RB9++KHCwsKaoEdoKEVFRfYnLQ0aNMihbTivNAxm9F3MjRs3JOmeT8Bp3bq1JOn69etOqdeqVSuH68F1OHusSHcv5Zo6daq++uorHTp0SOnp6VqzZo3Cw8NVWFioyZMn69ixY/XvPO5rDTH2YFxdunTRrFmz9PXXX+v777/X3r17tXLlSnXp0kV5eXl6+eWXHb68A67v9u3b+tOf/qRr164pMjJSv/vd7xzajvNKw2BG38VYrVZJkslkcsl6cB0N8W87YsSISssiIiIUERGhN954Q2lpaVq8eLH9hik8mDivoDZeeumlCj+3atVK7du314ABAzR+/HhlZWVp5cqVmjNnTtN0EE41d+5cpaenKzAwUAsXLnR4O84rDYMZfRdj+7Rq+2RbFdsnWVvb+tazrXOkHlyHs8dKTV577TVJ0r59+1ReXl7verh/NfbYgzF5eHho8uTJksTDIAzir3/9qzZs2CB/f38lJibW6h0JnFcaBkHfxXTq1EmSVFBQUG2bwsLCCm0dqXf+/Plq29jWOVIPrsPZY6UmISEhku6+DbG4uLje9XD/auyxB+OynVe4dOf+98EHH2jt2rVq166dEhMTKzwe0xGcVxoGQd/F2B5HdeLECd26davKNmazWZKqfHPp/woJCVGLFi10+fJlnTlzpso2hw8fdrgeXIezx0pNLl++bP/edl8HHkyNPfZgXLbzCjO097cFCxYoISFBvr6+SkhI0COPPFLrGpxXGgZB38UEBgaqe/fuKi8vV2pqaqX1GRkZKiwslL+/v/r27VtjPQ8PDw0cOFBS1S+tOHv2rLKystS8eXM99dRT9e4/Go+zx0pNtm3bJkkKDg6Wl5dXvevh/tXYYw/GZTuv9OjRo4l7grqKj4/XJ598Ih8fHyUkJKhLly51qsN5pWEQ9F2Q7ZrF+Ph4nT592r780qVLiouLkyTFxsbKze2//3xJSUmKjo7W9OnTK9WLjY2VyWTSxx9/bJ+9l+5e6zZz5kxZLBa98MIL8vb2bqhDQgNx5lgpKCjQli1bKj0v32q1atOmTfrwww8lVb6xDsa1aNEiRUdHa9GiRZXW1WXswbiqGytHjx7Vzp07defOnQrLb9++rYSEBK1du1YS55X71ZIlS7Rq1Sp5e3tr9erV9ln5e+G80rh46o4Lio6OVkxMjFJSUjRs2DANGDBA7u7uSk9PV0lJiaKiojRu3LgK2xQXFys/P7/KG1969eqlqVOnKj4+XmPGjFFERITatGmjzMxMXbp0Sb1799bbb7/dWIcHJ3LmWLly5YqmTZumuXPnKjg4WB07dlR5eblOnDhhf2bxuHHjNGbMmEY7PjhPTk6O/T9KScrLy5MkLV68WKtXr7YvX79+vf37oqIi5efnq6ioqFK9uow93B+cOVbOnTun119/Xb6+vurcubM6dOig69ev6/jx4/r555/l5uamadOm6Yknnmjgo4Kzffvtt/roo48kSQ8//LCSkpKqbBcSEmIP8BLnlcZG0HdR8+bNU79+/bRu3TplZGTIYrEoJCREo0aNUkxMTK0/zcbGxiosLEwJCQkym80qLS1VUFCQxo8fr0mTJsnDw6OBjgQNzVljJSAgQJMmTZLZbNaZM2eUl5cni8Uif39/DRkyRM8//7wiIyMb+GjQUEpKSvTDDz9UWm57e2VdOPs8BdfgzLESFhamCRMmyGw269y5czpy5IhMJpMCAgI0cuRIjR07lst27lNXrlyxf5+dna3s7Owq24WHh1cI+jXhvOJcJqvtwaUAAAAADIOPRQAAAIABEfQBAAAAAyLoAwAAAAZE0AcAAAAMiKAPAAAAGBBBHwAAADAgnqMPAAAA3MPJkye1d+9emc1mZWdn69SpU7JarVq6dKmio6Odtp/x48crIyOjxnYjR47U/Pnza2xH0AcAAADuISUlRZ9++mmD7+eJJ55Qp06dqlxXXl6urVu3SpL69+/vUD2CPgCggps3b2rjxo3as2ePjh07puLiYplMJrVr1049evTQ008/rcGDB6tFixZN3dV6Wb58uSTpxRdflLe3dxP3BoArCw0N1aRJk9SjRw/16NFD7777rkMz77V1r7cIf/PNN9q6davatGnj8F8RCPoAALsdO3Zozpw5Kioqsi9r1aqVTCaTzp07p3PnziktLU3x8fFasGCBIiMjm7C39bNixQpJ0nPPPUfQB3BPo0ePbuou6IsvvpAkDR061OGJFoI+AECS9OWXX+rdd9+VxWJRcHCw/vjHP2rgwIFq27atJOnatWvav3+/kpKSlJGRoYMHD97XQR8AGtqNGze0bt06paamKj8/X7dv31ZQUJCio6M1ceJEtW7d2qE658+f1/79+yVJv//97x3eP0EfAKBjx45p7ty5slgsevLJJ7Vs2bJKM0Zt2rTR4MGDNXjwYH3zzTcqLCxsot4CgOsrLCzUpEmTlJeXp3bt2qlv377y8PCQ2WzWihUrtH37dq1du1Y+Pj411vryyy9lsVgUFhamnj17OtwHHq8JANCSJUtUVlamDh06aNGiRTX+WXjIkCF6+eWXKywrKytTQkKCRo8erX79+qlXr14aPHiw5s+fX+FSoF9bvny5wsLCNGPGjGr3NWPGDIWFhdmvqbc5cOCAwsLC9Lvf/U6SdOjQIb366qvq37+/evXqpeHDhyspKUlWq7XKejZPP/20wsLC7F/36gsAOMJqteqtt95SXl6exo0bpx07duiTTz7RRx99pH/9618aPny4cnNzHXpyjtVq1caNGyXVbjZfIugDwAPvwoUL2rVrl6S7j3Zr06aNQ9uZTCb797/88ov+8Ic/6IMPPtDhw4dVVlYmd3d3nTp1SomJiRo6dKiysrIaovuS7s52jR8/Xrt379adO3dUWlqq3Nxc/eUvf9H7779foa2Xl5f8/PzsP7dt21Z+fn72Ly8vrwbrJ4AHw549e/T999+rT58+evfdd9WyZUv7uhYtWiguLk4PPfSQtmzZoitXrtyz1oEDB3T27Fl5eHho2LBhteoHQR8AHnAHDhywz3rbZsdra/r06Tpy5Ih8fHy0ZMkSZWVl6bvvvtOGDRsUGhqqK1eu6PXXX9cvv/zizK5LuvshY86cOYqJidG///1vHTx4UJmZmRo/frwkae3atTpx4oS9/axZs7Rv3z77zxs2bNC+ffvsX7NmzXJ6HwE8WPbs2SNJGjRokNzcKsftVq1aqUePHrp9+7bMZvM9a23YsEHS3b8+2u6ZchRBHwAecD/++KMkycPDQyEhIbXe/uDBg9q7d68kKT4+Xs8++6yaNWsmSerZs6cSEhLk4+Ojixcvau3atc7r+P938+ZNjRgxQrNnz7bP1Ht7e2vWrFkKDQ2V1WpVWlqa0/cLANU5e/asJGnBggUVLg389dfu3bsl6Z4TINeuXdP27dsl1f6yHYmbcQHggXf58mVJko+PT4XLcRyVmpoqSerRo4cGDhxYab2fn5/GjBmjlStXatu2bXrzzTfr1+EqvPrqq1Uuf/rpp3X8+PEKM/oA0NDu3LkjSQoPD6/2BVg2HTt2rHbd1q1bdevWLXXs2FEDBgyodT8I+gCAejly5Iike7+pMSIiQitXrtSpU6d048YNtWrVymn79/X1VVBQUJXrOnToIEm6evWq0/YHADUJDAyUJEVHR2vs2LF1rmN7dv7IkSOrvASoJly6AwAPOF9fX0nSlStXKj2hxhG2PzvbQnVVbOusVquKi4vr0Mvq3es51J6enpKk27dvO3WfAHAvtr9u2v7iWRfHjx+X2WyWyWTSyJEj61SDoA8AD7jf/OY3ku4+HvPkyZN1rlNWVuasLgHAfS0qKkrdu3dXRkaG5syZY79E8tfOnj2rdevWVVvDdhPugAEDarz8pzpcugMAD7jw8HCZTCZZrVbt2LHDHvwd1a5dO+Xn56ugoKDaNhcuXJB095Gcv35qhO2m3dLS0mq3vXbtWq36AwDOlpOTo7i4OPvPeXl5kqTFixdr9erV9uXr16+XJLm5uekf//iHYmNj9fnnn2vr1q3q0qWLAgICVFxcrIKCAp06dUp+fn5VXtpTXl6uzZs3S6rbTbg2BH0AeMAFBAToySef1K5du5SUlKSYmBiHniVvtVplMpnUrVs3HTp0SJmZmfZl/+s///mPJKlz584Vrs/39vaWpGrfsmu1WpWTk1OXw6qR7cNNXS5XAvBgKSkp0Q8//FBp+alTp6rdJiAgQBs2bNCGDRu0bds2HT9+XIcPH5avr6/at2+viRMn6plnnqly2x07dqi4uFi+vr6Kioqqc78J+gAAvfXWW9q/f78KCws1depULVu2zH59e1W++eYbFRYWauLEiYqOjrY/q/7bb7+t9J/SxYsX9dlnn0mSnn322QrrQkNDJUlms1k///yz2rdvX2H95s2bdf78eWccYiVeXl66du0afzEAUKP+/fsrNze31tt5enpq7Nixtb4hd/DgwXXa3//iGn0AgLp27ao5c+bIZDJp165dGjFihL766qsK15Veu3ZN//znPzV+/Hi9/fbbun79uiTp8ccf1xNPPCFJmjlzplJTU+2PlsvOztbEiRN15coV+fn5acKECRX2+9gsAHcVAAABt0lEQVRjj6l9+/YqLy/X1KlT7c+evnnzpj777DPNnj1bPj4+DXLMjzzyiCRp06ZN9v4CgJEwow8AkCSNHj1abdu21Zw5c3Ty5ElNnz5d0t03OJpMJnuwl6ROnTopIiLC/vOCBQs0ceJEHT16VG+++aY8PT3l7u5u38bHx0crVqyo9FZHd3d3zZkzR2+88YYyMjIUFRUlLy8v3bp1S7dv39aoUaNksVi0cePGBjne77//XmvWrNFnn32mhx56SCaTSYMHD9Y777zj9P0BQGMj6AMA7KKiojRgwABt3LhRu3fvVm5uroqLi2UymdSpUyf16NFDgwYN0qBBg+Th4WHfrl27dvr888+VnJysrVu3Kj8/X+Xl5ercubOefPJJvfLKK5Uuy7F55plntHr1an300UfKzs6WxWJRly5dNGbMGI0ePVozZsxokGO1fYhYv3698vLydP78+QZ5/CcANBWTlbuQAAAAAMPhGn0AAADAgAj6AAAAgAER9AEAAAADIugDAAAABkTQBwAAAAyIoA8AAAAYEEEfAAAAMCCCPgAAAGBABH0AAADAgAj6AAAAgAER9AEAAAAD+n8/TC3Jb25regAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "plt.hist(train_data['yield'])\n",
    "plt.title('Histogram of Yield Prediction in the training set')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Target value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleansing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train Data - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert date Object to datetime format\n",
    "train_data['date'] = pd.to_datetime(train_data['date'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Type Conversion\n",
    "train_data['farm_id'] = train_data['farm_id'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for duplicates\n",
    "train_data.duplicated(subset=None, keep= 'first').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop duplicates\n",
    "train_data1 = train_data.drop_duplicates(subset=None, keep= 'first', inplace=False, ignore_index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Farm Data - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop 'operations_commencing_year' column from the farm data\n",
    "farm_data.drop('operations_commencing_year', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Type Casting\n",
    "farm_data['farm_id'] = farm_data['farm_id'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "farm_id                     0\n",
       "num_processing_plants    1094\n",
       "farm_area                   0\n",
       "farming_company             0\n",
       "deidentified_location       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for Null Values\n",
    "farm_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fill missing values with median \n",
    "farm_data['num_processing_plants'].fillna(farm_data['num_processing_plants'].median(), inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Type Conversion\n",
    "farm_data['num_processing_plants'] = farm_data['num_processing_plants'].astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weather Data -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert date Object to datetime format\n",
    "train_weather['timestamp'] = pd.to_datetime(train_weather['timestamp'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping 'cloudiness' column\n",
    "train_weather.drop('cloudiness', axis = 1, inplace =  True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging train_data and farm_data on 'farm_id' column\n",
    "train_data_merged = pd.merge(train_data1,farm_data, on = 'farm_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>date</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>yield</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date     farm_id ingredient_type  yield  \\\n",
       "0 2016-01-01 00:00:00  fid_110884           ing_w    0.0   \n",
       "1 2016-01-01 01:00:00  fid_110884           ing_w    0.0   \n",
       "2 2016-01-01 02:00:00  fid_110884           ing_w    0.0   \n",
       "3 2016-01-01 03:00:00  fid_110884           ing_w    0.0   \n",
       "4 2016-01-01 04:00:00  fid_110884           ing_w    0.0   \n",
       "\n",
       "   num_processing_plants   farm_area farming_company deidentified_location  \n",
       "0                      7  690.455096     Obery Farms         location 7369  \n",
       "1                      7  690.455096     Obery Farms         location 7369  \n",
       "2                      7  690.455096     Obery Farms         location 7369  \n",
       "3                      7  690.455096     Obery Farms         location 7369  \n",
       "4                      7  690.455096     Obery Farms         location 7369  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming date column as timestamp\n",
    "train_data_merged.rename(columns = {'date':'timestamp'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location 7369    8784\n",
       "location 5677    8784\n",
       "location 2532    8783\n",
       "location 5833    8783\n",
       "location 5489    8783\n",
       "location 1784    8782\n",
       "location 565     8782\n",
       "location 8421    8780\n",
       "location 5290    8780\n",
       "location 5410    8777\n",
       "location 959     8763\n",
       "location 7048    8755\n",
       "location 6364    8755\n",
       "location 868     8614\n",
       "location 4525    8614\n",
       "location 5150    8454\n",
       "Name: deidentified_location, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for Value counts\n",
    "train_weather.deidentified_location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop deidentified_location column from the merged dataset\n",
    "train_data_merged.drop('deidentified_location', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20602665, 7)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139773, 8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_weather.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                0\n",
       "farm_id                  0\n",
       "ingredient_type          0\n",
       "yield                    0\n",
       "num_processing_plants    0\n",
       "farm_area                0\n",
       "farming_company          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_merged.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                   8784\n",
       "farm_id                     1434\n",
       "ingredient_type                4\n",
       "yield                    1688175\n",
       "num_processing_plants         18\n",
       "farm_area                   1397\n",
       "farming_company               16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_merged.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ing_w    59.769447\n",
       "ing_x    20.659056\n",
       "ing_y    13.308405\n",
       "ing_z     6.263093\n",
       "Name: ingredient_type, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_merged.ingredient_type.value_counts(normalize = True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Obery Farms                            40.450131\n",
       "Wayne Farms                            21.702649\n",
       "Sanderson Farms                        11.029656\n",
       "Dole Food Company                      10.588548\n",
       "Del Monte Foods                         8.382649\n",
       "Foster Farms                            2.061529\n",
       "Other                                   1.175683\n",
       "Freight Farms                           1.037710\n",
       "Monogram Foods                          0.610178\n",
       "Perdue Farms                            0.553763\n",
       "Tyson Foods                             0.546808\n",
       "Mountaire Farms                         0.542944\n",
       "Northland Organic Foods Corporation     0.468478\n",
       "West Liberty Foods                      0.376781\n",
       "Kansas Ville Farms                      0.272795\n",
       "Southern Confederate Farms              0.199697\n",
       "Name: farming_company, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_merged.farming_company.value_counts(normalize = True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                8784\n",
       "deidentified_location      16\n",
       "temp_obs                  619\n",
       "wind_direction             43\n",
       "dew_temp                  522\n",
       "pressure_sea_level        709\n",
       "precipitation             128\n",
       "wind_speed                 58\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_weather.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .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>timestamp</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>24.4</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>22.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>20.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp deidentified_location  temp_obs  wind_direction  \\\n",
       "0 2016-01-01 00:00:00         location 7369      25.0             0.0   \n",
       "1 2016-01-01 01:00:00         location 7369      24.4            70.0   \n",
       "2 2016-01-01 02:00:00         location 7369      22.8             0.0   \n",
       "3 2016-01-01 03:00:00         location 7369      21.1             0.0   \n",
       "4 2016-01-01 04:00:00         location 7369      20.0           250.0   \n",
       "\n",
       "   dew_temp  pressure_sea_level  precipitation  wind_speed  \n",
       "0      20.0              1019.7            NaN         0.0  \n",
       "1      21.1              1020.2           -1.0         1.5  \n",
       "2      21.1              1020.2            0.0         0.0  \n",
       "3      20.6              1020.1            0.0         0.0  \n",
       "4      20.0              1020.0           -1.0         2.6  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                datetime64[ns]\n",
       "deidentified_location            object\n",
       "temp_obs                        float64\n",
       "wind_direction                  float64\n",
       "dew_temp                        float64\n",
       "pressure_sea_level              float64\n",
       "precipitation                   float64\n",
       "wind_speed                      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_weather.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop 'timestamp' from weather data\n",
    "train_weather.drop('timestamp', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139773, 7)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_weather.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20602665, 7)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Final Merged Dataset \n",
    "train_final_merged = pd.merge(train_data_merged,train_weather,left_index = True, right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                8784\n",
       "farm_id                    14\n",
       "ingredient_type             2\n",
       "yield                    6456\n",
       "num_processing_plants       1\n",
       "farm_area                  14\n",
       "farming_company             4\n",
       "deidentified_location      16\n",
       "temp_obs                  619\n",
       "wind_direction             43\n",
       "dew_temp                  522\n",
       "pressure_sea_level        709\n",
       "precipitation             128\n",
       "wind_speed                 58\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ing_w    86.231962\n",
       "ing_x    13.768038\n",
       "Name: ingredient_type, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.ingredient_type.value_counts(normalize = True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Obery Farms          69.664384\n",
       "Dole Food Company    12.568951\n",
       "Wayne Farms          11.482189\n",
       "Sanderson Farms       6.284476\n",
       "Name: farming_company, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.farming_company.value_counts(normalize = True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location 7369    6.284476\n",
       "location 5677    6.284476\n",
       "location 2532    6.283760\n",
       "location 5833    6.283760\n",
       "location 5489    6.283760\n",
       "location 1784    6.283045\n",
       "location 565     6.283045\n",
       "location 8421    6.281614\n",
       "location 5290    6.281614\n",
       "location 5410    6.279467\n",
       "location 959     6.269451\n",
       "location 7048    6.263728\n",
       "location 6364    6.263728\n",
       "location 868     6.162850\n",
       "location 4525    6.162850\n",
       "location 5150    6.048378\n",
       "Name: deidentified_location, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.deidentified_location.value_counts(normalize = True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139773, 14)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                    0\n",
       "farm_id                      0\n",
       "ingredient_type              0\n",
       "yield                        0\n",
       "num_processing_plants        0\n",
       "farm_area                    0\n",
       "farming_company              0\n",
       "deidentified_location        0\n",
       "temp_obs                    55\n",
       "wind_direction            6268\n",
       "dew_temp                   113\n",
       "pressure_sea_level       10618\n",
       "precipitation            50289\n",
       "wind_speed                 304\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>yield</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>24.4</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>22.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>20.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp     farm_id ingredient_type  yield  \\\n",
       "0 2016-01-01 00:00:00  fid_110884           ing_w    0.0   \n",
       "1 2016-01-01 01:00:00  fid_110884           ing_w    0.0   \n",
       "2 2016-01-01 02:00:00  fid_110884           ing_w    0.0   \n",
       "3 2016-01-01 03:00:00  fid_110884           ing_w    0.0   \n",
       "4 2016-01-01 04:00:00  fid_110884           ing_w    0.0   \n",
       "\n",
       "   num_processing_plants   farm_area farming_company deidentified_location  \\\n",
       "0                      7  690.455096     Obery Farms         location 7369   \n",
       "1                      7  690.455096     Obery Farms         location 7369   \n",
       "2                      7  690.455096     Obery Farms         location 7369   \n",
       "3                      7  690.455096     Obery Farms         location 7369   \n",
       "4                      7  690.455096     Obery Farms         location 7369   \n",
       "\n",
       "   temp_obs  wind_direction  dew_temp  pressure_sea_level  precipitation  \\\n",
       "0      25.0             0.0      20.0              1019.7            NaN   \n",
       "1      24.4            70.0      21.1              1020.2           -1.0   \n",
       "2      22.8             0.0      21.1              1020.2            0.0   \n",
       "3      21.1             0.0      20.6              1020.1            0.0   \n",
       "4      20.0           250.0      20.0              1020.0           -1.0   \n",
       "\n",
       "   wind_speed  \n",
       "0         0.0  \n",
       "1         1.5  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         2.6  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-8.9</td>\n",
       "      <td>1007.4</td>\n",
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       "    <tr>\n",
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       "      <td>2016-09-20 22:00:00</td>\n",
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       "      <td>404.417</td>\n",
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       "      <td>2.2</td>\n",
       "      <td>170.0</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.6</td>\n",
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       "    <tr>\n",
       "      <th>139772</th>\n",
       "      <td>2016-09-21 00:00:00</td>\n",
       "      <td>fid_43573</td>\n",
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       "      <td>323.192</td>\n",
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       "      <td>1008.5</td>\n",
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      "text/plain": [
       "                 timestamp    farm_id ingredient_type    yield  \\\n",
       "139768 2016-09-20 20:00:00  fid_43573           ing_w  499.293   \n",
       "139769 2016-09-20 21:00:00  fid_43573           ing_w  447.077   \n",
       "139770 2016-09-20 22:00:00  fid_43573           ing_w  404.417   \n",
       "139771 2016-09-20 23:00:00  fid_43573           ing_w  359.368   \n",
       "139772 2016-09-21 00:00:00  fid_43573           ing_w  323.192   \n",
       "\n",
       "        num_processing_plants   farm_area farming_company  \\\n",
       "139768                      7  9232.70014     Obery Farms   \n",
       "139769                      7  9232.70014     Obery Farms   \n",
       "139770                      7  9232.70014     Obery Farms   \n",
       "139771                      7  9232.70014     Obery Farms   \n",
       "139772                      7  9232.70014     Obery Farms   \n",
       "\n",
       "       deidentified_location  temp_obs  wind_direction  dew_temp  \\\n",
       "139768         location 5150       3.0           180.0      -8.0   \n",
       "139769         location 5150       2.8           180.0      -8.9   \n",
       "139770         location 5150       2.8           180.0      -7.2   \n",
       "139771         location 5150       2.2           170.0      -6.7   \n",
       "139772         location 5150       1.7           180.0      -5.6   \n",
       "\n",
       "        pressure_sea_level  precipitation  wind_speed  \n",
       "139768                 NaN            NaN         5.7  \n",
       "139769              1007.4            NaN         7.7  \n",
       "139770              1007.5            NaN         5.1  \n",
       "139771              1008.0            NaN         4.6  \n",
       "139772              1008.5           -1.0         8.8  "
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   "source": [
    "train_final_merged.tail()"
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  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged.drop('farm_id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
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      ],
      "text/plain": [
       "            timestamp ingredient_type  yield  num_processing_plants  \\\n",
       "0 2016-01-01 00:00:00           ing_w    0.0                      7   \n",
       "1 2016-01-01 01:00:00           ing_w    0.0                      7   \n",
       "2 2016-01-01 02:00:00           ing_w    0.0                      7   \n",
       "3 2016-01-01 03:00:00           ing_w    0.0                      7   \n",
       "4 2016-01-01 04:00:00           ing_w    0.0                      7   \n",
       "\n",
       "    farm_area farming_company deidentified_location  temp_obs  wind_direction  \\\n",
       "0  690.455096     Obery Farms         location 7369      25.0             0.0   \n",
       "1  690.455096     Obery Farms         location 7369      24.4            70.0   \n",
       "2  690.455096     Obery Farms         location 7369      22.8             0.0   \n",
       "3  690.455096     Obery Farms         location 7369      21.1             0.0   \n",
       "4  690.455096     Obery Farms         location 7369      20.0           250.0   \n",
       "\n",
       "   dew_temp  pressure_sea_level  precipitation  wind_speed  \n",
       "0      20.0              1019.7            NaN         0.0  \n",
       "1      21.1              1020.2           -1.0         1.5  \n",
       "2      21.1              1020.2            0.0         0.0  \n",
       "3      20.6              1020.1            0.0         0.0  \n",
       "4      20.0              1020.0           -1.0         2.6  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139769</th>\n",
       "      <td>2016-09-20 21:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>447.077</td>\n",
       "      <td>7</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 5150</td>\n",
       "      <td>2.8</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>1007.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139770</th>\n",
       "      <td>2016-09-20 22:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>404.417</td>\n",
       "      <td>7</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 5150</td>\n",
       "      <td>2.8</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>1007.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139771</th>\n",
       "      <td>2016-09-20 23:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>359.368</td>\n",
       "      <td>7</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 5150</td>\n",
       "      <td>2.2</td>\n",
       "      <td>170.0</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139772</th>\n",
       "      <td>2016-09-21 00:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>323.192</td>\n",
       "      <td>7</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 5150</td>\n",
       "      <td>1.7</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp ingredient_type    yield  num_processing_plants  \\\n",
       "139768 2016-09-20 20:00:00           ing_w  499.293                      7   \n",
       "139769 2016-09-20 21:00:00           ing_w  447.077                      7   \n",
       "139770 2016-09-20 22:00:00           ing_w  404.417                      7   \n",
       "139771 2016-09-20 23:00:00           ing_w  359.368                      7   \n",
       "139772 2016-09-21 00:00:00           ing_w  323.192                      7   \n",
       "\n",
       "         farm_area farming_company deidentified_location  temp_obs  \\\n",
       "139768  9232.70014     Obery Farms         location 5150       3.0   \n",
       "139769  9232.70014     Obery Farms         location 5150       2.8   \n",
       "139770  9232.70014     Obery Farms         location 5150       2.8   \n",
       "139771  9232.70014     Obery Farms         location 5150       2.2   \n",
       "139772  9232.70014     Obery Farms         location 5150       1.7   \n",
       "\n",
       "        wind_direction  dew_temp  pressure_sea_level  precipitation  \\\n",
       "139768           180.0      -8.0                 NaN            NaN   \n",
       "139769           180.0      -8.9              1007.4            NaN   \n",
       "139770           180.0      -7.2              1007.5            NaN   \n",
       "139771           170.0      -6.7              1008.0            NaN   \n",
       "139772           180.0      -5.6              1008.5           -1.0   \n",
       "\n",
       "        wind_speed  \n",
       "139768         5.7  \n",
       "139769         7.7  \n",
       "139770         5.1  \n",
       "139771         4.6  \n",
       "139772         8.8  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating Hour difference between the timestamps\n",
    "train_final_merged['Hours'] = train_final_merged['timestamp'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_final_merged['Minutes'] = train_final_merged['timestamp'].dt.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_final_merged['Seconds'] = train_final_merged['timestamp'].dt.second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2016-01-01 00:00:00'),\n",
       " Timestamp('2016-12-31 23:00:00'),\n",
       " Timedelta('365 days 23:00:00'))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged['timestamp'].min(), train_final_merged['timestamp'].max(), (train_final_merged['timestamp'].max() -train_final_merged['timestamp'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unix time is a system for describing a point in time. It is the number of seconds that have elapsed since the Unix epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting Timestamp to integer\n",
    "train_final_merged['Unix Sec'] = pd.to_datetime(train_final_merged['timestamp']).astype(int)/ 10**9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>yield</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Hours</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.451606e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>24.4</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.451610e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>22.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.451614e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.451617e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>20.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.451621e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp ingredient_type  yield  num_processing_plants  \\\n",
       "0 2016-01-01 00:00:00           ing_w    0.0                      7   \n",
       "1 2016-01-01 01:00:00           ing_w    0.0                      7   \n",
       "2 2016-01-01 02:00:00           ing_w    0.0                      7   \n",
       "3 2016-01-01 03:00:00           ing_w    0.0                      7   \n",
       "4 2016-01-01 04:00:00           ing_w    0.0                      7   \n",
       "\n",
       "    farm_area farming_company deidentified_location  temp_obs  wind_direction  \\\n",
       "0  690.455096     Obery Farms         location 7369      25.0             0.0   \n",
       "1  690.455096     Obery Farms         location 7369      24.4            70.0   \n",
       "2  690.455096     Obery Farms         location 7369      22.8             0.0   \n",
       "3  690.455096     Obery Farms         location 7369      21.1             0.0   \n",
       "4  690.455096     Obery Farms         location 7369      20.0           250.0   \n",
       "\n",
       "   dew_temp  pressure_sea_level  precipitation  wind_speed  Hours  \\\n",
       "0      20.0              1019.7            NaN         0.0      0   \n",
       "1      21.1              1020.2           -1.0         1.5      1   \n",
       "2      21.1              1020.2            0.0         0.0      2   \n",
       "3      20.6              1020.1            0.0         0.0      3   \n",
       "4      20.0              1020.0           -1.0         2.6      4   \n",
       "\n",
       "       Unix Sec  \n",
       "0  1.451606e+09  \n",
       "1  1.451610e+09  \n",
       "2  1.451614e+09  \n",
       "3  1.451617e+09  \n",
       "4  1.451621e+09  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                datetime64[ns]\n",
       "ingredient_type                  object\n",
       "yield                           float64\n",
       "num_processing_plants             int64\n",
       "farm_area                       float64\n",
       "farming_company                  object\n",
       "deidentified_location            object\n",
       "temp_obs                        float64\n",
       "wind_direction                  float64\n",
       "dew_temp                        float64\n",
       "pressure_sea_level              float64\n",
       "precipitation                   float64\n",
       "wind_speed                      float64\n",
       "Hours                             int64\n",
       "Unix Sec                        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Type Conversion float to int\n",
    "train_final_merged['Unix Sec'] = train_final_merged['Unix Sec'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                datetime64[ns]\n",
       "ingredient_type                  object\n",
       "yield                           float64\n",
       "num_processing_plants             int64\n",
       "farm_area                       float64\n",
       "farming_company                  object\n",
       "deidentified_location            object\n",
       "temp_obs                        float64\n",
       "wind_direction                  float64\n",
       "dew_temp                        float64\n",
       "pressure_sea_level              float64\n",
       "precipitation                   float64\n",
       "wind_speed                      float64\n",
       "Hours                             int64\n",
       "Unix Sec                          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged.drop('timestamp', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Practices in  Data Cleaning -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* I have cleaned the data by checking for duplicate values and dropped them from that trainset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Performed Type casting on the columns respectively wherever needed and also checked for misssing values in the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Determined if all of the columns in test exist in train, and vice versa w.r.t target, \n",
    "Did some initial plotting ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Merged all the three datasets into the final training set which is given to the model to learn and can predict on much larger data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To save complexity , removed/dropped few columns from the data ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding for Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Necessary Import\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged.farming_company = le.fit_transform(train_final_merged.farming_company)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged['farming_company'] = train_final_merged['farming_company'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged.deidentified_location = le.fit_transform(train_final_merged.deidentified_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged['deidentified_location'] = train_final_merged['deidentified_location'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged.ingredient_type = le.fit_transform(train_final_merged.ingredient_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged['ingredient_type'] = train_final_merged['ingredient_type'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ingredient_type          category\n",
       "yield                     float64\n",
       "num_processing_plants       int64\n",
       "farm_area                 float64\n",
       "farming_company          category\n",
       "deidentified_location    category\n",
       "temp_obs                  float64\n",
       "wind_direction            float64\n",
       "dew_temp                  float64\n",
       "pressure_sea_level        float64\n",
       "precipitation             float64\n",
       "wind_speed                float64\n",
       "Hours                       int64\n",
       "Unix Sec                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ingredient_type             2\n",
       "yield                    6456\n",
       "num_processing_plants       1\n",
       "farm_area                  14\n",
       "farming_company             4\n",
       "deidentified_location      16\n",
       "temp_obs                  619\n",
       "wind_direction             43\n",
       "dew_temp                  522\n",
       "pressure_sea_level        709\n",
       "precipitation             128\n",
       "wind_speed                 58\n",
       "Hours                      24\n",
       "Unix Sec                 8784\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    97372\n",
       "0    17568\n",
       "3    16049\n",
       "2     8784\n",
       "Name: farming_company, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.farming_company.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>yield</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Hours</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451606400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>24.4</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1451610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1451613600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1451617200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4</td>\n",
       "      <td>1451620800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ingredient_type  yield  num_processing_plants   farm_area farming_company  \\\n",
       "0               0    0.0                      7  690.455096               1   \n",
       "1               0    0.0                      7  690.455096               1   \n",
       "2               0    0.0                      7  690.455096               1   \n",
       "3               0    0.0                      7  690.455096               1   \n",
       "4               0    0.0                      7  690.455096               1   \n",
       "\n",
       "  deidentified_location  temp_obs  wind_direction  dew_temp  \\\n",
       "0                    12      25.0             0.0      20.0   \n",
       "1                    12      24.4            70.0      21.1   \n",
       "2                    12      22.8             0.0      21.1   \n",
       "3                    12      21.1             0.0      20.6   \n",
       "4                    12      20.0           250.0      20.0   \n",
       "\n",
       "   pressure_sea_level  precipitation  wind_speed  Hours    Unix Sec  \n",
       "0              1019.7            NaN         0.0      0  1451606400  \n",
       "1              1020.2           -1.0         1.5      1  1451610000  \n",
       "2              1020.2            0.0         0.0      2  1451613600  \n",
       "3              1020.1            0.0         0.0      3  1451617200  \n",
       "4              1020.0           -1.0         2.6      4  1451620800  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12    8784\n",
       "8     8784\n",
       "9     8783\n",
       "6     8783\n",
       "1     8783\n",
       "7     8782\n",
       "0     8782\n",
       "13    8780\n",
       "4     8780\n",
       "5     8777\n",
       "15    8763\n",
       "11    8755\n",
       "10    8755\n",
       "14    8614\n",
       "2     8614\n",
       "3     8454\n",
       "Name: deidentified_location, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.deidentified_location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    120529\n",
       "1     19244\n",
       "Name: ingredient_type, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.ingredient_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ingredient_type             2\n",
       "yield                    6456\n",
       "num_processing_plants       1\n",
       "farm_area                  14\n",
       "farming_company             4\n",
       "deidentified_location      16\n",
       "temp_obs                  619\n",
       "wind_direction             43\n",
       "dew_temp                  522\n",
       "pressure_sea_level        709\n",
       "precipitation             128\n",
       "wind_speed                 58\n",
       "Hours                      24\n",
       "Unix Sec                 8784\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yield</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Hours</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>139773.000000</td>\n",
       "      <td>139773.0</td>\n",
       "      <td>139773.000000</td>\n",
       "      <td>139718.000000</td>\n",
       "      <td>133505.000000</td>\n",
       "      <td>139660.000000</td>\n",
       "      <td>129155.000000</td>\n",
       "      <td>89484.000000</td>\n",
       "      <td>139469.000000</td>\n",
       "      <td>139773.000000</td>\n",
       "      <td>1.397730e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>503.861098</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6451.949394</td>\n",
       "      <td>14.418106</td>\n",
       "      <td>180.526632</td>\n",
       "      <td>7.350158</td>\n",
       "      <td>1016.158038</td>\n",
       "      <td>0.983047</td>\n",
       "      <td>3.560527</td>\n",
       "      <td>11.502615</td>\n",
       "      <td>1.467400e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>981.401152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8196.029748</td>\n",
       "      <td>10.626595</td>\n",
       "      <td>111.523629</td>\n",
       "      <td>9.790235</td>\n",
       "      <td>7.629684</td>\n",
       "      <td>8.463678</td>\n",
       "      <td>2.335874</td>\n",
       "      <td>6.921604</td>\n",
       "      <td>8.867605e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>252.696160</td>\n",
       "      <td>-28.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-35.000000</td>\n",
       "      <td>968.200000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.451606e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>743.224000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1011.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.459908e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>114.670000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3446.701300</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>1016.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.467310e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>478.475000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9232.700140</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>1020.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.474798e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8442.070000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34445.924019</td>\n",
       "      <td>47.200000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>1045.500000</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.483225e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               yield  num_processing_plants      farm_area       temp_obs  \\\n",
       "count  139773.000000               139773.0  139773.000000  139718.000000   \n",
       "mean      503.861098                    7.0    6451.949394      14.418106   \n",
       "std       981.401152                    0.0    8196.029748      10.626595   \n",
       "min         0.000000                    7.0     252.696160     -28.900000   \n",
       "25%         0.000000                    7.0     743.224000       7.200000   \n",
       "50%       114.670000                    7.0    3446.701300      15.000000   \n",
       "75%       478.475000                    7.0    9232.700140      22.200000   \n",
       "max      8442.070000                    7.0   34445.924019      47.200000   \n",
       "\n",
       "       wind_direction       dew_temp  pressure_sea_level  precipitation  \\\n",
       "count   133505.000000  139660.000000       129155.000000   89484.000000   \n",
       "mean       180.526632       7.350158         1016.158038       0.983047   \n",
       "std        111.523629       9.790235            7.629684       8.463678   \n",
       "min          0.000000     -35.000000          968.200000      -1.000000   \n",
       "25%         80.000000       0.600000         1011.800000       0.000000   \n",
       "50%        190.000000       8.300000         1016.400000       0.000000   \n",
       "75%        280.000000      14.400000         1020.800000       0.000000   \n",
       "max        360.000000      26.100000         1045.500000     343.000000   \n",
       "\n",
       "          wind_speed          Hours      Unix Sec  \n",
       "count  139469.000000  139773.000000  1.397730e+05  \n",
       "mean        3.560527      11.502615  1.467400e+09  \n",
       "std         2.335874       6.921604  8.867605e+06  \n",
       "min         0.000000       0.000000  1.451606e+09  \n",
       "25%         2.100000       6.000000  1.459908e+09  \n",
       "50%         3.100000      12.000000  1.467310e+09  \n",
       "75%         5.000000      18.000000  1.474798e+09  \n",
       "max        19.000000      23.000000  1.483225e+09  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptive Stats of Final Dataset\n",
    "train_final_merged.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ingredient_type              0\n",
       "yield                        0\n",
       "num_processing_plants        0\n",
       "farm_area                    0\n",
       "farming_company              0\n",
       "deidentified_location        0\n",
       "temp_obs                    55\n",
       "wind_direction            6268\n",
       "dew_temp                   113\n",
       "pressure_sea_level       10618\n",
       "precipitation            50289\n",
       "wind_speed                 304\n",
       "Hours                        0\n",
       "Unix Sec                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null values in the merged dataset\n",
    "train_final_merged.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ingredient_type          category\n",
       "yield                     float64\n",
       "num_processing_plants       int64\n",
       "farm_area                 float64\n",
       "farming_company          category\n",
       "deidentified_location    category\n",
       "temp_obs                  float64\n",
       "wind_direction            float64\n",
       "dew_temp                  float64\n",
       "pressure_sea_level        float64\n",
       "precipitation             float64\n",
       "wind_speed                float64\n",
       "Hours                       int64\n",
       "Unix Sec                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_final_merged.drop('Hours', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    139773\n",
       "Name: num_processing_plants, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.num_processing_plants.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "    <tr>\n",
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       "      <td>24.4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>22.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1451613600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1451617200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1451620800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ingredient_type  yield  num_processing_plants   farm_area farming_company  \\\n",
       "0               0    0.0                      7  690.455096               1   \n",
       "1               0    0.0                      7  690.455096               1   \n",
       "2               0    0.0                      7  690.455096               1   \n",
       "3               0    0.0                      7  690.455096               1   \n",
       "4               0    0.0                      7  690.455096               1   \n",
       "\n",
       "  deidentified_location  temp_obs  wind_direction  dew_temp  \\\n",
       "0                    12      25.0             0.0      20.0   \n",
       "1                    12      24.4            70.0      21.1   \n",
       "2                    12      22.8             0.0      21.1   \n",
       "3                    12      21.1             0.0      20.6   \n",
       "4                    12      20.0           250.0      20.0   \n",
       "\n",
       "   pressure_sea_level  precipitation  wind_speed    Unix Sec  \n",
       "0              1019.7            NaN         0.0  1451606400  \n",
       "1              1020.2           -1.0         1.5  1451610000  \n",
       "2              1020.2            0.0         0.0  1451613600  \n",
       "3              1020.1            0.0         0.0  1451617200  \n",
       "4              1020.0           -1.0         2.6  1451620800  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seperated the numeric columns from the final merged dataset\n",
    "num_col = ['yield','farm_area','temp_obs', 'wind_direction', 'dew_temp', 'pressure_sea_level', \n",
    "       'precipitation', 'wind_speed','Unix Sec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['yield',\n",
       " 'farm_area',\n",
       " 'temp_obs',\n",
       " 'wind_direction',\n",
       " 'dew_temp',\n",
       " 'pressure_sea_level',\n",
       " 'precipitation',\n",
       " 'wind_speed',\n",
       " 'Unix Sec']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Plot on the numeric data\n",
    "plt.figure(figsize=(22,10))\n",
    "data = train_final_merged\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "corrMatrix = df.corr(method = 'pearson',min_periods = 1)\n",
    "sns.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing the Numeric Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imputation\n",
    "num_imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "imputed_data = pd.DataFrame(num_imputer.fit_transform(train_final_merged[num_col]),\n",
    "                               columns = num_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yield                 float64\n",
       "farm_area             float64\n",
       "temp_obs              float64\n",
       "wind_direction        float64\n",
       "dew_temp              float64\n",
       "pressure_sea_level    float64\n",
       "precipitation         float64\n",
       "wind_speed            float64\n",
       "Unix Sec              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "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>yield</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.451606e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>24.4</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.451610e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>22.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.451614e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.451617e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>20.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.451621e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yield   farm_area  temp_obs  wind_direction  dew_temp  pressure_sea_level  \\\n",
       "0    0.0  690.455096      25.0             0.0      20.0              1019.7   \n",
       "1    0.0  690.455096      24.4            70.0      21.1              1020.2   \n",
       "2    0.0  690.455096      22.8             0.0      21.1              1020.2   \n",
       "3    0.0  690.455096      21.1             0.0      20.6              1020.1   \n",
       "4    0.0  690.455096      20.0           250.0      20.0              1020.0   \n",
       "\n",
       "   precipitation  wind_speed      Unix Sec  \n",
       "0            0.0         0.0  1.451606e+09  \n",
       "1           -1.0         1.5  1.451610e+09  \n",
       "2            0.0         0.0  1.451614e+09  \n",
       "3            0.0         0.0  1.451617e+09  \n",
       "4           -1.0         2.6  1.451621e+09  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yield</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139768</th>\n",
       "      <td>499.293</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>3.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>1016.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>1.474402e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139769</th>\n",
       "      <td>447.077</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>2.8</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>1007.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>1.474405e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139770</th>\n",
       "      <td>404.417</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>2.8</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>1007.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.474409e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139771</th>\n",
       "      <td>359.368</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>2.2</td>\n",
       "      <td>170.0</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.474412e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139772</th>\n",
       "      <td>323.192</td>\n",
       "      <td>9232.70014</td>\n",
       "      <td>1.7</td>\n",
       "      <td>180.0</td>\n",
       "      <td>-5.6</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1.474416e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          yield   farm_area  temp_obs  wind_direction  dew_temp  \\\n",
       "139768  499.293  9232.70014       3.0           180.0      -8.0   \n",
       "139769  447.077  9232.70014       2.8           180.0      -8.9   \n",
       "139770  404.417  9232.70014       2.8           180.0      -7.2   \n",
       "139771  359.368  9232.70014       2.2           170.0      -6.7   \n",
       "139772  323.192  9232.70014       1.7           180.0      -5.6   \n",
       "\n",
       "        pressure_sea_level  precipitation  wind_speed      Unix Sec  \n",
       "139768              1016.4            0.0         5.7  1.474402e+09  \n",
       "139769              1007.4            0.0         7.7  1.474405e+09  \n",
       "139770              1007.5            0.0         5.1  1.474409e+09  \n",
       "139771              1008.0            0.0         4.6  1.474412e+09  \n",
       "139772              1008.5           -1.0         8.8  1.474416e+09  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Type Conversions\n",
    "col = ['temp_obs','dew_temp','wind_speed','wind_direction','Unix Sec','farm_area']\n",
    "\n",
    "imputed_data[col] = imputed_data[col].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yield</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1451606400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>21</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1451610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451613600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451617200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>20</td>\n",
       "      <td>250</td>\n",
       "      <td>20</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1451620800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   yield  farm_area  temp_obs  wind_direction  dew_temp  pressure_sea_level  \\\n",
       "0    0.0        690        25               0        20              1019.7   \n",
       "1    0.0        690        24              70        21              1020.2   \n",
       "2    0.0        690        22               0        21              1020.2   \n",
       "3    0.0        690        21               0        20              1020.1   \n",
       "4    0.0        690        20             250        20              1020.0   \n",
       "\n",
       "   precipitation  wind_speed    Unix Sec  \n",
       "0            0.0           0  1451606400  \n",
       "1           -1.0           1  1451610000  \n",
       "2            0.0           0  1451613600  \n",
       "3            0.0           0  1451617200  \n",
       "4           -1.0           2  1451620800  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yield</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139768</th>\n",
       "      <td>499.293</td>\n",
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       "      <td>180</td>\n",
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       "      <td>5</td>\n",
       "      <td>1474401600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139769</th>\n",
       "      <td>447.077</td>\n",
       "      <td>9232</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>-8</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1474405200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139770</th>\n",
       "      <td>404.417</td>\n",
       "      <td>9232</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>-7</td>\n",
       "      <td>1007.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1474408800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139771</th>\n",
       "      <td>359.368</td>\n",
       "      <td>9232</td>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "      <td>-6</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1474412400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139772</th>\n",
       "      <td>323.192</td>\n",
       "      <td>9232</td>\n",
       "      <td>1</td>\n",
       "      <td>180</td>\n",
       "      <td>-5</td>\n",
       "      <td>1008.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1474416000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          yield  farm_area  temp_obs  wind_direction  dew_temp  \\\n",
       "139768  499.293       9232         3             180        -8   \n",
       "139769  447.077       9232         2             180        -8   \n",
       "139770  404.417       9232         2             180        -7   \n",
       "139771  359.368       9232         2             170        -6   \n",
       "139772  323.192       9232         1             180        -5   \n",
       "\n",
       "        pressure_sea_level  precipitation  wind_speed    Unix Sec  \n",
       "139768              1016.4            0.0           5  1474401600  \n",
       "139769              1007.4            0.0           7  1474405200  \n",
       "139770              1007.5            0.0           5  1474408800  \n",
       "139771              1008.0            0.0           4  1474412400  \n",
       "139772              1008.5           -1.0           8  1474416000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yield                 float64\n",
       "farm_area               int64\n",
       "temp_obs                int64\n",
       "wind_direction          int64\n",
       "dew_temp                int64\n",
       "pressure_sea_level    float64\n",
       "precipitation         float64\n",
       "wind_speed              int64\n",
       "Unix Sec                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139773, 9)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputed_data.insert(0, 'id', imputed_data.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>yield</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1020.2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1451610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451613600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451617200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>20</td>\n",
       "      <td>250</td>\n",
       "      <td>20</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1451620800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  yield  farm_area  temp_obs  wind_direction  dew_temp  \\\n",
       "0   0    0.0        690        25               0        20   \n",
       "1   1    0.0        690        24              70        21   \n",
       "2   2    0.0        690        22               0        21   \n",
       "3   3    0.0        690        21               0        20   \n",
       "4   4    0.0        690        20             250        20   \n",
       "\n",
       "   pressure_sea_level  precipitation  wind_speed    Unix Sec  \n",
       "0              1019.7            0.0           0  1451606400  \n",
       "1              1020.2           -1.0           1  1451610000  \n",
       "2              1020.2            0.0           0  1451613600  \n",
       "3              1020.1            0.0           0  1451617200  \n",
       "4              1020.0           -1.0           2  1451620800  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col = ['ingredient_type','farming_company', 'deidentified_location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_data = pd.merge(imputed_data,\n",
    "                         train_final_merged[cat_col],\n",
    "                         left_index=True,\n",
    "                         right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>yield</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451606400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "      <td>21</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1451610000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1020.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451613600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>1020.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1451617200</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>690</td>\n",
       "      <td>20</td>\n",
       "      <td>250</td>\n",
       "      <td>20</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1451620800</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  yield  farm_area  temp_obs  wind_direction  dew_temp  \\\n",
       "0   0    0.0        690        25               0        20   \n",
       "1   1    0.0        690        24              70        21   \n",
       "2   2    0.0        690        22               0        21   \n",
       "3   3    0.0        690        21               0        20   \n",
       "4   4    0.0        690        20             250        20   \n",
       "\n",
       "   pressure_sea_level  precipitation  wind_speed    Unix Sec ingredient_type  \\\n",
       "0              1019.7            0.0           0  1451606400               0   \n",
       "1              1020.2           -1.0           1  1451610000               0   \n",
       "2              1020.2            0.0           0  1451613600               0   \n",
       "3              1020.1            0.0           0  1451617200               0   \n",
       "4              1020.0           -1.0           2  1451620800               0   \n",
       "\n",
       "  farming_company deidentified_location  \n",
       "0               1                    12  \n",
       "1               1                    12  \n",
       "2               1                    12  \n",
       "3               1                    12  \n",
       "4               1                    12  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          int64\n",
       "yield                     float64\n",
       "farm_area                   int64\n",
       "temp_obs                    int64\n",
       "wind_direction              int64\n",
       "dew_temp                    int64\n",
       "pressure_sea_level        float64\n",
       "precipitation             float64\n",
       "wind_speed                  int64\n",
       "Unix Sec                    int64\n",
       "ingredient_type          category\n",
       "farming_company          category\n",
       "deidentified_location    category\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building and Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Train Test Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Necessary Import\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(111818, 12) (27955, 12)\n",
      "(111818,) (27955,)\n"
     ]
    }
   ],
   "source": [
    "y = Final_data['yield']\n",
    "X = Final_data.loc[:, Final_data.columns != 'yield']\n",
    "\n",
    "X_train, X_test, y_train, y_test =   train_test_split(X, y, test_size=0.20,random_state = 123)\n",
    "\n",
    "print(X_train.shape, X_test.shape)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a Base Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Because we are attempting to predict a value we will look at Regression methods. Because of the medium size of our data we will begin RandomForestRegressors,\n",
    "and then move towards ensemble methods like boosted trees such as GradientBoostingRegressor.\n",
    "\n",
    "\n",
    "\n",
    "* To give ourself a baseline our first submission will simply use all the data and no hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred1 = rf.predict(X_train)\n",
    "test_pred1 = rf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([395.85771,  98.41559,   0.     , ...,   0.     ,   0.     ,\n",
       "       139.11096])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.      , 298.32675 , 409.43725 , ...,  16.999835,   1.160352,\n",
       "         0.      ])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9856948315532165\n",
      "0.8962162167393667\n"
     ]
    }
   ],
   "source": [
    "print(rf.score(X_train, y_train))\n",
    "print(rf.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Custom Function \n",
    "def convert_for_sklearn(label_list):\n",
    "    return [1 if i == 1 else 0 for i in label_list]\n",
    "\n",
    "\n",
    "def rmse_metric(y_true, y_pred):\n",
    "    \n",
    "    y_test_scoring = convert_for_sklearn(y_true)\n",
    "    test_pred_scoring = convert_for_sklearn(y_pred)\n",
    "\n",
    "    mse = mean_squared_error(y_true= y_test, y_pred = test_pred1)\n",
    "    rmse = np.sqrt(mse)\n",
    "    \n",
    "    print(\"Test RMSE_score: \",rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE_score:  310.32330939137864\n"
     ]
    }
   ],
   "source": [
    "rmse_metric(y_true = y_test, y_pred = test_pred1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper Parameter Tuning and Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hyperparameter tuning on the all features data ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters currently in use:\n",
      "\n",
      "{'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': 100,\n",
      " 'n_jobs': None,\n",
      " 'oob_score': False,\n",
      " 'random_state': None,\n",
      " 'verbose': 0,\n",
      " 'warm_start': False}\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "# Look at parameters used by our current forest\n",
    "print('Parameters currently in use:\\n')\n",
    "pprint(rf.get_params())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Random search is a technique where random combinations of the hyperparameters are used to find the best solution for the built model. It is similar to grid search, and yet it has proven to yield better results comparatively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import randomized search\n",
    "from sklearn.model_selection import RandomizedSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use the random grid to search for best hyperparameters\n",
    "\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [100,150,200]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [5,10,15,20,25,30]\n",
    "# max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10, 15]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 5, 10]\n",
    "# Method of selecting samples for training each tree\n",
    "# bootstrap = [True, False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': [5, 10, 15, 20, 25, 30],\n",
      " 'max_features': ['auto', 'sqrt'],\n",
      " 'min_samples_leaf': [1, 2, 5, 10],\n",
      " 'min_samples_split': [2, 5, 10, 15],\n",
      " 'n_estimators': [100, 150, 200]}\n"
     ]
    }
   ],
   "source": [
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, \n",
    "                               cv = 3, verbose=2, random_state=42, n_jobs = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=15 \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=100, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=15, total=  22.7s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=15 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   22.7s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=15, total=  22.4s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=15, total=  22.7s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=10, total=  27.6s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=10, total=  27.2s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=10 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=10, total=  28.0s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=20, total=  11.9s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=20, total=  11.8s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=20, total=  11.8s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=30, total=  27.2s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=30, total=  26.8s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=30, total=  27.6s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=25, total=  26.8s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=25, total=  28.1s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=2, max_features=auto, max_depth=25, total=  27.3s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=25, total=  42.8s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=25, total=  41.9s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=25, total=  42.3s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  10.7s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  10.6s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  10.7s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=5, total=  20.4s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=5, total=  20.4s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=5 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=5, max_features=auto, max_depth=5, total=  20.6s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=sqrt, max_depth=5, total=   4.9s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=sqrt, max_depth=5, total=   4.8s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=sqrt, max_depth=5, total=   4.7s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=20, total=  14.4s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=20, total=  14.2s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=20, total=  14.2s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=25, total=  14.3s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=25, total=  14.4s\n",
      "[CV] n_estimators=200, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=200, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=25, total=  14.5s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=25, total=   8.8s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=25, total=   8.7s\n",
      "[CV] n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=25, total=   8.8s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=sqrt, max_depth=15, total=  13.6s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=sqrt, max_depth=15, total=  13.4s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=sqrt, max_depth=15, total=  13.5s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=  16.0s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=  16.0s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=20 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=2, max_features=sqrt, max_depth=20, total=  16.0s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=30 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=30, total=   8.4s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=30 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=30, total=   8.5s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=30 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=30, total=   8.5s\n",
      "[CV] n_estimators=200, min_samples_split=15, min_samples_leaf=2, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=15, min_samples_leaf=2, max_features=sqrt, max_depth=10, total=  11.0s\n",
      "[CV] n_estimators=200, min_samples_split=15, min_samples_leaf=2, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=15, min_samples_leaf=2, max_features=sqrt, max_depth=10, total=  10.9s\n",
      "[CV] n_estimators=200, min_samples_split=15, min_samples_leaf=2, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=15, min_samples_leaf=2, max_features=sqrt, max_depth=10, total=  10.9s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=15, total=  10.3s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=15, total=  10.1s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=15, total=  10.4s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   4.7s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   4.8s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=5, total=   4.7s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=30, total=  48.7s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=30, total=  48.3s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=10, max_features=auto, max_depth=30, total=  48.4s\n",
      "[CV] n_estimators=100, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=100, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   5.6s\n",
      "[CV] n_estimators=100, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=100, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   5.4s\n",
      "[CV] n_estimators=100, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=100, min_samples_split=15, min_samples_leaf=1, max_features=sqrt, max_depth=10, total=   5.5s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=30 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=30, total=  10.9s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=30 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=30, total=  10.7s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=30 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=10, max_features=sqrt, max_depth=30, total=  10.8s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   3.2s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   3.1s\n",
      "[CV] n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=100, min_samples_split=10, min_samples_leaf=10, max_features=sqrt, max_depth=5, total=   3.2s\n",
      "[CV] n_estimators=100, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=25, total=  26.0s\n",
      "[CV] n_estimators=100, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=25, total=  26.0s\n",
      "[CV] n_estimators=100, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=100, min_samples_split=15, min_samples_leaf=2, max_features=auto, max_depth=25, total=  26.9s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=10, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=10, max_features=auto, max_depth=15, total=  23.3s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=10, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=10, max_features=auto, max_depth=15, total=  22.4s\n",
      "[CV] n_estimators=100, min_samples_split=2, min_samples_leaf=10, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=100, min_samples_split=2, min_samples_leaf=10, max_features=auto, max_depth=15, total=  22.8s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=auto, max_depth=30, total=  39.1s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=auto, max_depth=30, total=  38.0s\n",
      "[CV] n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=auto, max_depth=30 \n",
      "[CV]  n_estimators=150, min_samples_split=15, min_samples_leaf=5, max_features=auto, max_depth=30, total=  38.6s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  10.8s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  11.0s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=10 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=10, total=  10.9s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=  10.8s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=  10.7s\n",
      "[CV] n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=10, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=  10.8s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=25, total=  48.0s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=25, total=  47.1s\n",
      "[CV] n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=200, min_samples_split=5, min_samples_leaf=10, max_features=auto, max_depth=25, total=  48.4s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=15, total=  10.6s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=15, total=  10.3s\n",
      "[CV] n_estimators=150, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=150, min_samples_split=5, min_samples_leaf=5, max_features=sqrt, max_depth=15, total=  10.6s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=5, total=   4.9s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=5, total=   4.7s\n",
      "[CV] n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=5 \n",
      "[CV]  n_estimators=150, min_samples_split=2, min_samples_leaf=5, max_features=sqrt, max_depth=5, total=   4.8s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=30 \n",
      "[CV]  n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=30, total=  15.9s\n",
      "[CV] n_estimators=200, min_samples_split=10, min_samples_leaf=5, max_features=sqrt, max_depth=30 \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-123-1663bb339ee4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrf_random\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    734\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 736\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    738\u001b[0m         \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1529\u001b[0m         evaluate_candidates(ParameterSampler(\n\u001b[1;32m   1530\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_distributions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1531\u001b[0;31m             random_state=self.random_state))\n\u001b[0m",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    713\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 715\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    716\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1005\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    833\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    834\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 835\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    836\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    752\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    755\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 256\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 256\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    529\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    390\u001b[0m                     \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m                     n_samples_bootstrap=n_samples_bootstrap)\n\u001b[0;32m--> 392\u001b[0;31m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m    393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m             \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1005\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    833\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    834\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 835\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    836\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    752\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 754\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    755\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 256\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 256\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[1;32m    166\u001b[0m                                                         indices=indices)\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1244\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1245\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1246\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1247\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/share/anaconda3/lib/python3.7/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    373\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rf_random.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuned Random Forest Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Best parameters set for the Random Forest Regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_reg = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=30,\n",
    "          max_features='sqrt', max_leaf_nodes=None,\n",
    "          min_impurity_decrease=0.0, min_impurity_split=None,          \n",
    "                               min_samples_leaf=2, min_samples_split=5,\n",
    "          min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
    "          oob_score=False, random_state=None, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.3 s, sys: 284 ms, total: 25.6 s\n",
      "Wall time: 25.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=30, max_features='sqrt', min_samples_leaf=2,\n",
       "                      min_samples_split=5, n_estimators=200, n_jobs=1)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "RF_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred = RF_reg.predict(X_train)\n",
    "test_pred = RF_reg.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.91203474e+02, 9.57916015e+01, 0.00000000e+00, ...,\n",
       "       2.25243750e-02, 1.15153750e+00, 1.36395602e+02])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.70641667e-03, 2.94891387e+02, 3.88998779e+02, ...,\n",
       "       1.75672962e+01, 1.87047849e+00, 3.16434400e-01])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9562703995445271\n",
      "0.8723426911529972\n"
     ]
    }
   ],
   "source": [
    "print(RF_reg.score(X_train, y_train))\n",
    "print(RF_reg.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining Custom Function \n",
    "def convert_for_sklearn(label_list):\n",
    "    return [1 if i == 1 else 0 for i in label_list]\n",
    "\n",
    "\n",
    "def rmse_metric(y_true, y_pred):\n",
    "    \n",
    "    y_test_scoring = convert_for_sklearn(y_true)\n",
    "    test_pred_scoring = convert_for_sklearn(y_pred)\n",
    "\n",
    "    mse = mean_squared_error(y_true= y_test, y_pred = test_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    \n",
    "    print(\"Test RMSE_score: \",rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE_score:  344.1695875856751\n"
     ]
    }
   ],
   "source": [
    "rmse_metric(y_true = y_test, y_pred = test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gbm = GradientBoostingRegressor(n_estimators= 400,\n",
    "                                 learning_rate=0.1,\n",
    "                                 max_depth = 5,\n",
    "                                min_samples_split = 2,\n",
    "                                 random_state=0,\n",
    "                                 warm_start = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 36s, sys: 315 ms, total: 1min 36s\n",
      "Wall time: 1min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "gbm_base_model = gbm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred2 = gbm.predict(X_train)\n",
    "test_pred2 = gbm.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([422.1496657 , 103.67534629,  -1.66623057, ...,  -1.56107355,\n",
       "        16.04138555, 133.5744567 ])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -7.22866943, 331.20035933, 348.45012131, ...,  34.50790149,\n",
       "         4.92207188,   7.9871877 ])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8814756541117789\n",
      "0.842737014557192\n"
     ]
    }
   ],
   "source": [
    "print(gbm.score(X_train, y_train))\n",
    "print(gbm.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluation of Random Search\n",
    "def evaluate(gbm, X_test, y_test):\n",
    "    predictions = gbm.predict(X_test)\n",
    "    errors = np.sqrt(mean_squared_error(y_test, test_pred2))\n",
    "    print('Model Performance')\n",
    "    print('RMSE of: ', errors)\n",
    "    \n",
    "    return errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "RMSE of:  381.9995989840124\n"
     ]
    }
   ],
   "source": [
    "base_accuracy = evaluate(gbm, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions on the Test Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the Test Data Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the test data files\n",
    "test_weather = pd.read_csv('test_weather.csv')\n",
    "test_data = pd.read_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_90053</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_17537</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_110392</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_62402</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date     farm_id ingredient_type  id\n",
       "0  2017-01-01 00:00:00  fid_110884           ing_w   0\n",
       "1  2017-01-01 00:00:00   fid_90053           ing_w   1\n",
       "2  2017-01-01 00:00:00   fid_17537           ing_w   2\n",
       "3  2017-01-01 00:00:00  fid_110392           ing_w   3\n",
       "4  2017-01-01 00:00:00   fid_62402           ing_w   4"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>timestamp</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>cloudiness</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1021.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>17.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1021.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>17.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>16.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp deidentified_location  temp_obs  cloudiness  \\\n",
       "0  2017-01-01 00:00:00         location 7369      17.8         4.0   \n",
       "1  2017-01-01 01:00:00         location 7369      17.8         2.0   \n",
       "2  2017-01-01 02:00:00         location 7369      16.1         0.0   \n",
       "3  2017-01-01 03:00:00         location 7369      17.2         0.0   \n",
       "4  2017-01-01 04:00:00         location 7369      16.7         2.0   \n",
       "\n",
       "   wind_direction  dew_temp  pressure_sea_level  precipitation  wind_speed  \n",
       "0           100.0      11.7              1021.4            NaN         3.6  \n",
       "1           130.0      12.8              1022.0            0.0         3.1  \n",
       "2           140.0      12.8              1021.9            0.0         3.1  \n",
       "3           140.0      13.3              1022.2            0.0         3.1  \n",
       "4           130.0      13.3              1022.3            0.0         2.6  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20848800, 4)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Performed all the Data Cleaning practices on the Test datasets as well "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['date'] = pd.to_datetime(test_data['date'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['farm_id'] = test_data['farm_id'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_data.duplicated(subset=None, keep= 'first').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_data1 = test_data.drop_duplicates(subset=None, keep= 'first', inplace=False, ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_weather['timestamp'] = pd.to_datetime(test_weather['timestamp'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_weather.drop('cloudiness', axis = 1, inplace =  True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_merged = pd.merge(test_data,farm_data, on = 'farm_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>date</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>129</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>258</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>387</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>516</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date     farm_id ingredient_type   id  num_processing_plants  \\\n",
       "0 2017-01-01 00:00:00  fid_110884           ing_w    0                      7   \n",
       "1 2017-01-01 01:00:00  fid_110884           ing_w  129                      7   \n",
       "2 2017-01-01 02:00:00  fid_110884           ing_w  258                      7   \n",
       "3 2017-01-01 03:00:00  fid_110884           ing_w  387                      7   \n",
       "4 2017-01-01 04:00:00  fid_110884           ing_w  516                      7   \n",
       "\n",
       "    farm_area farming_company deidentified_location  \n",
       "0  690.455096     Obery Farms         location 7369  \n",
       "1  690.455096     Obery Farms         location 7369  \n",
       "2  690.455096     Obery Farms         location 7369  \n",
       "3  690.455096     Obery Farms         location 7369  \n",
       "4  690.455096     Obery Farms         location 7369  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_merged.rename(columns = {'date':'timestamp'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_data_merged.drop('timestamp', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_data_merged.drop('deidentified_location', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_weather.drop('timestamp', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_weather.drop('deidentified_location', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21251760, 8)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1021.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.8</td>\n",
       "      <td>130.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.1</td>\n",
       "      <td>140.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1021.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.2</td>\n",
       "      <td>140.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.7</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   temp_obs  wind_direction  dew_temp  pressure_sea_level  precipitation  \\\n",
       "0      17.8           100.0      11.7              1021.4            NaN   \n",
       "1      17.8           130.0      12.8              1022.0            0.0   \n",
       "2      16.1           140.0      12.8              1021.9            0.0   \n",
       "3      17.2           140.0      13.3              1022.2            0.0   \n",
       "4      16.7           130.0      13.3              1022.3            0.0   \n",
       "\n",
       "   wind_speed  \n",
       "0         3.6  \n",
       "1         3.1  \n",
       "2         3.1  \n",
       "3         3.1  \n",
       "4         2.6  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21251755</th>\n",
       "      <td>2017-12-28 07:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673195</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251756</th>\n",
       "      <td>2017-12-28 08:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673445</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251757</th>\n",
       "      <td>2017-12-28 09:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673695</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251758</th>\n",
       "      <td>2017-12-28 10:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673945</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251759</th>\n",
       "      <td>2017-12-28 11:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41674195</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timestamp    farm_id ingredient_type        id  \\\n",
       "21251755 2017-12-28 07:00:00  fid_34139           ing_w  41673195   \n",
       "21251756 2017-12-28 08:00:00  fid_34139           ing_w  41673445   \n",
       "21251757 2017-12-28 09:00:00  fid_34139           ing_w  41673695   \n",
       "21251758 2017-12-28 10:00:00  fid_34139           ing_w  41673945   \n",
       "21251759 2017-12-28 11:00:00  fid_34139           ing_w  41674195   \n",
       "\n",
       "          num_processing_plants    farm_area farming_company  \\\n",
       "21251755                      7  8572.252713     Wayne Farms   \n",
       "21251756                      7  8572.252713     Wayne Farms   \n",
       "21251757                      7  8572.252713     Wayne Farms   \n",
       "21251758                      7  8572.252713     Wayne Farms   \n",
       "21251759                      7  8572.252713     Wayne Farms   \n",
       "\n",
       "         deidentified_location  \n",
       "21251755         location 5150  \n",
       "21251756         location 5150  \n",
       "21251757         location 5150  \n",
       "21251758         location 5150  \n",
       "21251759         location 5150  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_merged.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>129</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>258</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>387</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>516</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp     farm_id ingredient_type   id  num_processing_plants  \\\n",
       "0 2017-01-01 00:00:00  fid_110884           ing_w    0                      7   \n",
       "1 2017-01-01 01:00:00  fid_110884           ing_w  129                      7   \n",
       "2 2017-01-01 02:00:00  fid_110884           ing_w  258                      7   \n",
       "3 2017-01-01 03:00:00  fid_110884           ing_w  387                      7   \n",
       "4 2017-01-01 04:00:00  fid_110884           ing_w  516                      7   \n",
       "\n",
       "    farm_area farming_company deidentified_location  \n",
       "0  690.455096     Obery Farms         location 7369  \n",
       "1  690.455096     Obery Farms         location 7369  \n",
       "2  690.455096     Obery Farms         location 7369  \n",
       "3  690.455096     Obery Farms         location 7369  \n",
       "4  690.455096     Obery Farms         location 7369  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#test_data_merged.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_weather.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_weather.insert(0, 'id', test_weather.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1021.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>17.8</td>\n",
       "      <td>130.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>16.1</td>\n",
       "      <td>140.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1021.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>140.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16.7</td>\n",
       "      <td>130.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  temp_obs  wind_direction  dew_temp  pressure_sea_level  precipitation  \\\n",
       "0   0      17.8           100.0      11.7              1021.4            NaN   \n",
       "1   1      17.8           130.0      12.8              1022.0            0.0   \n",
       "2   2      16.1           140.0      12.8              1021.9            0.0   \n",
       "3   3      17.2           140.0      13.3              1022.2            0.0   \n",
       "4   4      16.7           130.0      13.3              1022.3            0.0   \n",
       "\n",
       "   wind_speed  \n",
       "0         3.6  \n",
       "1         3.1  \n",
       "2         3.1  \n",
       "3         3.1  \n",
       "4         2.6  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2017-01-01 00:00:00</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>129</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>258</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>387</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>516</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp     farm_id ingredient_type   id  num_processing_plants  \\\n",
       "0 2017-01-01 00:00:00  fid_110884           ing_w    0                      7   \n",
       "1 2017-01-01 01:00:00  fid_110884           ing_w  129                      7   \n",
       "2 2017-01-01 02:00:00  fid_110884           ing_w  258                      7   \n",
       "3 2017-01-01 03:00:00  fid_110884           ing_w  387                      7   \n",
       "4 2017-01-01 04:00:00  fid_110884           ing_w  516                      7   \n",
       "\n",
       "    farm_area farming_company deidentified_location  \n",
       "0  690.455096     Obery Farms         location 7369  \n",
       "1  690.455096     Obery Farms         location 7369  \n",
       "2  690.455096     Obery Farms         location 7369  \n",
       "3  690.455096     Obery Farms         location 7369  \n",
       "4  690.455096     Obery Farms         location 7369  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21251755</th>\n",
       "      <td>2017-12-28 07:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673195</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251756</th>\n",
       "      <td>2017-12-28 08:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673445</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251757</th>\n",
       "      <td>2017-12-28 09:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673695</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251758</th>\n",
       "      <td>2017-12-28 10:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41673945</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251759</th>\n",
       "      <td>2017-12-28 11:00:00</td>\n",
       "      <td>fid_34139</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>41674195</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>Wayne Farms</td>\n",
       "      <td>location 5150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timestamp    farm_id ingredient_type        id  \\\n",
       "21251755 2017-12-28 07:00:00  fid_34139           ing_w  41673195   \n",
       "21251756 2017-12-28 08:00:00  fid_34139           ing_w  41673445   \n",
       "21251757 2017-12-28 09:00:00  fid_34139           ing_w  41673695   \n",
       "21251758 2017-12-28 10:00:00  fid_34139           ing_w  41673945   \n",
       "21251759 2017-12-28 11:00:00  fid_34139           ing_w  41674195   \n",
       "\n",
       "          num_processing_plants    farm_area farming_company  \\\n",
       "21251755                      7  8572.252713     Wayne Farms   \n",
       "21251756                      7  8572.252713     Wayne Farms   \n",
       "21251757                      7  8572.252713     Wayne Farms   \n",
       "21251758                      7  8572.252713     Wayne Farms   \n",
       "21251759                      7  8572.252713     Wayne Farms   \n",
       "\n",
       "         deidentified_location  \n",
       "21251755         location 5150  \n",
       "21251756         location 5150  \n",
       "21251757         location 5150  \n",
       "21251758         location 5150  \n",
       "21251759         location 5150  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_merged.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test_weather.drop('deidentified_location', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21251760, 8)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(138825, 7)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_weather.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge = pd.merge(test_data_merged,test_weather,on = 'id',how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21251760, 14)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final_merge.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>farm_id</th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>17.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1021.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>129</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1014.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>258</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>22.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 03:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>387</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>17.8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>1024.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 04:00:00</td>\n",
       "      <td>fid_110884</td>\n",
       "      <td>ing_w</td>\n",
       "      <td>516</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>Obery Farms</td>\n",
       "      <td>location 7369</td>\n",
       "      <td>20.6</td>\n",
       "      <td>170.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp     farm_id ingredient_type   id  num_processing_plants  \\\n",
       "0 2017-01-01 00:00:00  fid_110884           ing_w    0                      7   \n",
       "1 2017-01-01 01:00:00  fid_110884           ing_w  129                      7   \n",
       "2 2017-01-01 02:00:00  fid_110884           ing_w  258                      7   \n",
       "3 2017-01-01 03:00:00  fid_110884           ing_w  387                      7   \n",
       "4 2017-01-01 04:00:00  fid_110884           ing_w  516                      7   \n",
       "\n",
       "    farm_area farming_company deidentified_location  temp_obs  wind_direction  \\\n",
       "0  690.455096     Obery Farms         location 7369      17.8           100.0   \n",
       "1  690.455096     Obery Farms         location 7369      11.7             0.0   \n",
       "2  690.455096     Obery Farms         location 7369      22.2             NaN   \n",
       "3  690.455096     Obery Farms         location 7369      17.8           120.0   \n",
       "4  690.455096     Obery Farms         location 7369      20.6           170.0   \n",
       "\n",
       "   dew_temp  pressure_sea_level  precipitation  wind_speed  \n",
       "0      11.7              1021.4            NaN         3.6  \n",
       "1      10.6              1014.7            0.0         0.0  \n",
       "2      12.8              1027.0            0.0         2.1  \n",
       "3      13.9              1024.4            0.0         2.1  \n",
       "4      18.3              1007.6            0.0         4.1  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge.drop('farm_id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2017-01-01 00:00:00'),\n",
       " Timestamp('2017-12-31 23:00:00'),\n",
       " Timedelta('364 days 23:00:00'))"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final_merge['timestamp'].min(), test_final_merge['timestamp'].max(), (test_final_merge['timestamp'].max() -test_final_merge['timestamp'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                       0\n",
       "ingredient_type                 0\n",
       "id                              0\n",
       "num_processing_plants           0\n",
       "farm_area                       0\n",
       "farming_company                 0\n",
       "deidentified_location           0\n",
       "temp_obs                 21111902\n",
       "wind_direction           21118036\n",
       "dew_temp                 21112044\n",
       "pressure_sea_level       21122825\n",
       "precipitation            21160021\n",
       "wind_speed               21112100\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final_merge.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge['Unix Sec'] = pd.to_datetime(test_final_merge['timestamp']).astype(int)/ 10**9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge['Unix Sec'] = test_final_merge['Unix Sec'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge['Hours'] = test_final_merge['timestamp'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge.drop('timestamp', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge.farming_company = le.fit_transform(test_final_merge.farming_company)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge['farming_company'] = test_final_merge['farming_company'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge.deidentified_location = le.fit_transform(test_final_merge.deidentified_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge['deidentified_location'] = test_final_merge['deidentified_location'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge.ingredient_type = le.fit_transform(test_final_merge.ingredient_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge['ingredient_type'] = test_final_merge['ingredient_type'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_final_merge.drop('Hours', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21251755</th>\n",
       "      <td>0</td>\n",
       "      <td>41673195</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514444400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251756</th>\n",
       "      <td>0</td>\n",
       "      <td>41673445</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514448000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251757</th>\n",
       "      <td>0</td>\n",
       "      <td>41673695</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514451600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251758</th>\n",
       "      <td>0</td>\n",
       "      <td>41673945</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514455200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251759</th>\n",
       "      <td>0</td>\n",
       "      <td>41674195</td>\n",
       "      <td>7</td>\n",
       "      <td>8572.252713</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514458800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ingredient_type        id  num_processing_plants    farm_area  \\\n",
       "21251755               0  41673195                      7  8572.252713   \n",
       "21251756               0  41673445                      7  8572.252713   \n",
       "21251757               0  41673695                      7  8572.252713   \n",
       "21251758               0  41673945                      7  8572.252713   \n",
       "21251759               0  41674195                      7  8572.252713   \n",
       "\n",
       "         farming_company deidentified_location  temp_obs  wind_direction  \\\n",
       "21251755              14                     3       NaN             NaN   \n",
       "21251756              14                     3       NaN             NaN   \n",
       "21251757              14                     3       NaN             NaN   \n",
       "21251758              14                     3       NaN             NaN   \n",
       "21251759              14                     3       NaN             NaN   \n",
       "\n",
       "          dew_temp  pressure_sea_level  precipitation  wind_speed    Unix Sec  \n",
       "21251755       NaN                 NaN            NaN         NaN  1514444400  \n",
       "21251756       NaN                 NaN            NaN         NaN  1514448000  \n",
       "21251757       NaN                 NaN            NaN         NaN  1514451600  \n",
       "21251758       NaN                 NaN            NaN         NaN  1514455200  \n",
       "21251759       NaN                 NaN            NaN         NaN  1514458800  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_final_merge.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ingredient_type        id  num_processing_plants    farm_area  \\\n",
      "0                      0         0                      7   690.455096   \n",
      "1                      0       129                      7   690.455096   \n",
      "2                      0       258                      7   690.455096   \n",
      "3                      0       387                      7   690.455096   \n",
      "4                      0       516                      7   690.455096   \n",
      "...                  ...       ...                    ...          ...   \n",
      "20848795               0  41673149                      7  3830.948108   \n",
      "20848796               0  41673399                      7  3830.948108   \n",
      "20848797               0  41673649                      7  3830.948108   \n",
      "20848798               0  41673899                      7  3830.948108   \n",
      "20848799               0  41674149                      7  3830.948108   \n",
      "\n",
      "         farming_company deidentified_location  temp_obs  wind_direction  \\\n",
      "0                      8                    12      17.8           100.0   \n",
      "1                      8                    12      11.7             0.0   \n",
      "2                      8                    12      22.2             NaN   \n",
      "3                      8                    12      17.8           120.0   \n",
      "4                      8                    12      20.6           170.0   \n",
      "...                  ...                   ...       ...             ...   \n",
      "20848795               1                     3       NaN             NaN   \n",
      "20848796               1                     3       NaN             NaN   \n",
      "20848797               1                     3       NaN             NaN   \n",
      "20848798               1                     3       NaN             NaN   \n",
      "20848799               1                     3       NaN             NaN   \n",
      "\n",
      "          dew_temp  pressure_sea_level  precipitation  wind_speed    Unix Sec  \n",
      "0             11.7              1021.4            NaN         3.6  1483228800  \n",
      "1             10.6              1014.7            0.0         0.0  1483232400  \n",
      "2             12.8              1027.0            0.0         2.1  1483236000  \n",
      "3             13.9              1024.4            0.0         2.1  1483239600  \n",
      "4             18.3              1007.6            0.0         4.1  1483243200  \n",
      "...            ...                 ...            ...         ...         ...  \n",
      "20848795       NaN                 NaN            NaN         NaN  1514444400  \n",
      "20848796       NaN                 NaN            NaN         NaN  1514448000  \n",
      "20848797       NaN                 NaN            NaN         NaN  1514451600  \n",
      "20848798       NaN                 NaN            NaN         NaN  1514455200  \n",
      "20848799       NaN                 NaN            NaN         NaN  1514458800  \n",
      "\n",
      "[20848800 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "# Number of rows to drop \n",
    "n = 402960\n",
    "  \n",
    "# Removing last n rows \n",
    "Test_Data = test_final_merge.iloc[:-n] \n",
    "  \n",
    "# Printing dataframe \n",
    "print(Test_Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>17.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1021.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1483228800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1014.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1483232400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>22.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1483236000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>387</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>17.8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>1024.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1483239600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>516</td>\n",
       "      <td>7</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>20.6</td>\n",
       "      <td>170.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1483243200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ingredient_type   id  num_processing_plants   farm_area farming_company  \\\n",
       "0               0    0                      7  690.455096               8   \n",
       "1               0  129                      7  690.455096               8   \n",
       "2               0  258                      7  690.455096               8   \n",
       "3               0  387                      7  690.455096               8   \n",
       "4               0  516                      7  690.455096               8   \n",
       "\n",
       "  deidentified_location  temp_obs  wind_direction  dew_temp  \\\n",
       "0                    12      17.8           100.0      11.7   \n",
       "1                    12      11.7             0.0      10.6   \n",
       "2                    12      22.2             NaN      12.8   \n",
       "3                    12      17.8           120.0      13.9   \n",
       "4                    12      20.6           170.0      18.3   \n",
       "\n",
       "   pressure_sea_level  precipitation  wind_speed    Unix Sec  \n",
       "0              1021.4            NaN         3.6  1483228800  \n",
       "1              1014.7            0.0         0.0  1483232400  \n",
       "2              1027.0            0.0         2.1  1483236000  \n",
       "3              1024.4            0.0         2.1  1483239600  \n",
       "4              1007.6            0.0         4.1  1483243200  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ingredient_type                 4\n",
       "id                       20445840\n",
       "num_processing_plants          18\n",
       "farm_area                    1366\n",
       "farming_company                16\n",
       "deidentified_location          16\n",
       "temp_obs                      612\n",
       "wind_direction                 53\n",
       "dew_temp                      528\n",
       "pressure_sea_level            713\n",
       "precipitation                 144\n",
       "wind_speed                     70\n",
       "Unix Sec                     8760\n",
       "dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_Data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ingredient_type</th>\n",
       "      <th>id</th>\n",
       "      <th>num_processing_plants</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>farming_company</th>\n",
       "      <th>deidentified_location</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20848795</th>\n",
       "      <td>0</td>\n",
       "      <td>41673149</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514448000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848797</th>\n",
       "      <td>0</td>\n",
       "      <td>41673649</td>\n",
       "      <td>7</td>\n",
       "      <td>3830.948108</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514451600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848798</th>\n",
       "      <td>0</td>\n",
       "      <td>41673899</td>\n",
       "      <td>7</td>\n",
       "      <td>3830.948108</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514455200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848799</th>\n",
       "      <td>0</td>\n",
       "      <td>41674149</td>\n",
       "      <td>7</td>\n",
       "      <td>3830.948108</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514458800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ingredient_type        id  num_processing_plants    farm_area  \\\n",
       "20848795               0  41673149                      7  3830.948108   \n",
       "20848796               0  41673399                      7  3830.948108   \n",
       "20848797               0  41673649                      7  3830.948108   \n",
       "20848798               0  41673899                      7  3830.948108   \n",
       "20848799               0  41674149                      7  3830.948108   \n",
       "\n",
       "         farming_company deidentified_location  temp_obs  wind_direction  \\\n",
       "20848795               1                     3       NaN             NaN   \n",
       "20848796               1                     3       NaN             NaN   \n",
       "20848797               1                     3       NaN             NaN   \n",
       "20848798               1                     3       NaN             NaN   \n",
       "20848799               1                     3       NaN             NaN   \n",
       "\n",
       "          dew_temp  pressure_sea_level  precipitation  wind_speed    Unix Sec  \n",
       "20848795       NaN                 NaN            NaN         NaN  1514444400  \n",
       "20848796       NaN                 NaN            NaN         NaN  1514448000  \n",
       "20848797       NaN                 NaN            NaN         NaN  1514451600  \n",
       "20848798       NaN                 NaN            NaN         NaN  1514455200  \n",
       "20848799       NaN                 NaN            NaN         NaN  1514458800  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Test_Data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col = ['id','farm_area','temp_obs', 'wind_direction', 'dew_temp', 'pressure_sea_level', \n",
    "       'precipitation', 'wind_speed','Unix Sec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_imputer = SimpleImputer(strategy = 'median')\n",
    "#\n",
    "imputed_data_test = pd.DataFrame(num_imputer.fit_transform(Test_Data[num_col]),\n",
    "                               columns = num_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>id</th>\n",
       "      <th>farm_area</th>\n",
       "      <th>temp_obs</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>dew_temp</th>\n",
       "      <th>pressure_sea_level</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>Unix Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>17.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1021.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.483229e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>129.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1014.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.483232e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>258.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>22.2</td>\n",
       "      <td>190.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.483236e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>387.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>17.8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>1024.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.483240e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>516.0</td>\n",
       "      <td>690.455096</td>\n",
       "      <td>20.6</td>\n",
       "      <td>170.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.483243e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id   farm_area  temp_obs  wind_direction  dew_temp  pressure_sea_level  \\\n",
       "0    0.0  690.455096      17.8           100.0      11.7              1021.4   \n",
       "1  129.0  690.455096      11.7             0.0      10.6              1014.7   \n",
       "2  258.0  690.455096      22.2           190.0      12.8              1027.0   \n",
       "3  387.0  690.455096      17.8           120.0      13.9              1024.4   \n",
       "4  516.0  690.455096      20.6           170.0      18.3              1007.6   \n",
       "\n",
       "   precipitation  wind_speed      Unix Sec  \n",
       "0            0.0         3.6  1.483229e+09  \n",
       "1            0.0         0.0  1.483232e+09  \n",
       "2            0.0         2.1  1.483236e+09  \n",
       "3            0.0         2.1  1.483240e+09  \n",
       "4            0.0         4.1  1.483243e+09  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputed_data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['id','temp_obs','dew_temp','wind_speed','wind_direction','Unix Sec','farm_area']\n",
    "\n",
    "imputed_data_test[col] = imputed_data_test[col].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_col = ['ingredient_type','farming_company', 'deidentified_location']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_data_test = pd.merge(imputed_data_test, \n",
    "                         Test_Data[cat_col], \n",
    "                         left_index=True,\n",
    "                         right_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20848800, 12)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Final_data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Final_data_test.duplicated(subset=None, keep= 'first').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Test_Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Test_Data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Final_data_test1 = Final_data_test.drop_duplicates(subset=None, keep= 'first', inplace=False, ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = RF_reg.predict(Final_data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([157.56816553, 185.78149272, 154.52699923, ..., 341.72878908,\n",
       "       341.72878908, 341.72878908])"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred_1 = np.round(test_pred,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([157.568, 185.781, 154.527, 153.929, 180.692, 177.514, 162.862,\n",
       "       167.836, 172.696, 162.309])"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred_1[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_sub = pd.read_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20848800, 2)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>yield</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  yield\n",
       "0   0      0\n",
       "1   1      0\n",
       "2   2      0\n",
       "3   3      0\n",
       "4   4      0"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20848795</th>\n",
       "      <td>41696845</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848796</th>\n",
       "      <td>41696846</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848797</th>\n",
       "      <td>41696847</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848798</th>\n",
       "      <td>41696848</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848799</th>\n",
       "      <td>41696849</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  yield\n",
       "20848795  41696845      0\n",
       "20848796  41696846      0\n",
       "20848797  41696847      0\n",
       "20848798  41696848      0\n",
       "20848799  41696849      0"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_sub.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission  = pd.DataFrame({'id': test_data.id, 'yield': test_pred_1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>157.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>185.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>154.527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>153.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>180.692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    yield\n",
       "0   0  157.568\n",
       "1   1  185.781\n",
       "2   2  154.527\n",
       "3   3  153.929\n",
       "4   4  180.692"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20848795</th>\n",
       "      <td>41696845</td>\n",
       "      <td>341.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848796</th>\n",
       "      <td>41696846</td>\n",
       "      <td>341.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848797</th>\n",
       "      <td>41696847</td>\n",
       "      <td>341.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848798</th>\n",
       "      <td>41696848</td>\n",
       "      <td>341.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20848799</th>\n",
       "      <td>41696849</td>\n",
       "      <td>341.729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id    yield\n",
       "20848795  41696845  341.729\n",
       "20848796  41696846  341.729\n",
       "20848797  41696847  341.729\n",
       "20848798  41696848  341.729\n",
       "20848799  41696849  341.729"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff599d0f50>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "submission['yield'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the Dataframe to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('/home/3679b81a/predict_submission.csv', index = None)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}