Optimizing Agricultural production .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "49b264eb",
   "metadata": {},
   "source": [
    "# Agricultural Production Optimization Engine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed6e50e",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4215fcd2",
   "metadata": {},
   "source": [
    "#### Build a Predictive Model so as to suggest the most suitable Crops to grow based on the available Climatic and Soil condition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b059e6b",
   "metadata": {},
   "source": [
    "## Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d684771",
   "metadata": {},
   "source": [
    "#### Achieve Precision Farming by Optimizing the Agricultural Production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38bcf153",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "15d49ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for interactivity\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d34e5073",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the dataset\n",
    "df=pd.read_csv(\"Crop_recommendation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72a2a9ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets checks teh shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16f7ef7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>20.879744</td>\n",
       "      <td>82.002744</td>\n",
       "      <td>6.502985</td>\n",
       "      <td>202.935536</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>21.770462</td>\n",
       "      <td>80.319644</td>\n",
       "      <td>7.038096</td>\n",
       "      <td>226.655537</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>44</td>\n",
       "      <td>23.004459</td>\n",
       "      <td>82.320763</td>\n",
       "      <td>7.840207</td>\n",
       "      <td>263.964248</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>26.491096</td>\n",
       "      <td>80.158363</td>\n",
       "      <td>6.980401</td>\n",
       "      <td>242.864034</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>20.130175</td>\n",
       "      <td>81.604873</td>\n",
       "      <td>7.628473</td>\n",
       "      <td>262.717340</td>\n",
       "      <td>rice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    N   P   K  temperature   humidity        ph    rainfall label\n",
       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets check the head of the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a61275cb",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
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       "      <th>2195</th>\n",
       "      <td>107</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>26.774637</td>\n",
       "      <td>66.413269</td>\n",
       "      <td>6.780064</td>\n",
       "      <td>177.774507</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2196</th>\n",
       "      <td>99</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>27.417112</td>\n",
       "      <td>56.636362</td>\n",
       "      <td>6.086922</td>\n",
       "      <td>127.924610</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2197</th>\n",
       "      <td>118</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>24.131797</td>\n",
       "      <td>67.225123</td>\n",
       "      <td>6.362608</td>\n",
       "      <td>173.322839</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>117</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>26.272418</td>\n",
       "      <td>52.127394</td>\n",
       "      <td>6.758793</td>\n",
       "      <td>127.175293</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2199</th>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>23.603016</td>\n",
       "      <td>60.396475</td>\n",
       "      <td>6.779833</td>\n",
       "      <td>140.937041</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        N   P   K  temperature   humidity        ph    rainfall   label\n",
       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee\n",
       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee\n",
       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee\n",
       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee\n",
       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets check the tail of the dataset\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8f4d7bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N              0\n",
       "P              0\n",
       "K              0\n",
       "temperature    0\n",
       "humidity       0\n",
       "ph             0\n",
       "rainfall       0\n",
       "label          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lest check if there is my missing value present in the dataset\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebc01881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2200 entries, 0 to 2199\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   N            2200 non-null   int64  \n",
      " 1   P            2200 non-null   int64  \n",
      " 2   K            2200 non-null   int64  \n",
      " 3   temperature  2200 non-null   float64\n",
      " 4   humidity     2200 non-null   float64\n",
      " 5   ph           2200 non-null   float64\n",
      " 6   rainfall     2200 non-null   float64\n",
      " 7   label        2200 non-null   object \n",
      "dtypes: float64(4), int64(3), object(1)\n",
      "memory usage: 137.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#lets check the info is this dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7521ba47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>ph</th>\n",
       "      <th>rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>50.551818</td>\n",
       "      <td>53.362727</td>\n",
       "      <td>48.149091</td>\n",
       "      <td>25.616244</td>\n",
       "      <td>71.481779</td>\n",
       "      <td>6.469480</td>\n",
       "      <td>103.463655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36.917334</td>\n",
       "      <td>32.985883</td>\n",
       "      <td>50.647931</td>\n",
       "      <td>5.063749</td>\n",
       "      <td>22.263812</td>\n",
       "      <td>0.773938</td>\n",
       "      <td>54.958389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.825675</td>\n",
       "      <td>14.258040</td>\n",
       "      <td>3.504752</td>\n",
       "      <td>20.211267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.769375</td>\n",
       "      <td>60.261953</td>\n",
       "      <td>5.971693</td>\n",
       "      <td>64.551686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>25.598693</td>\n",
       "      <td>80.473146</td>\n",
       "      <td>6.425045</td>\n",
       "      <td>94.867624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>84.250000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>28.561654</td>\n",
       "      <td>89.948771</td>\n",
       "      <td>6.923643</td>\n",
       "      <td>124.267508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>43.675493</td>\n",
       "      <td>99.981876</td>\n",
       "      <td>9.935091</td>\n",
       "      <td>298.560117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 N            P            K  temperature     humidity  \\\n",
       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
       "\n",
       "                ph     rainfall  \n",
       "count  2200.000000  2200.000000  \n",
       "mean      6.469480   103.463655  \n",
       "std       0.773938    54.958389  \n",
       "min       3.504752    20.211267  \n",
       "25%       5.971693    64.551686  \n",
       "50%       6.425045    94.867624  \n",
       "75%       6.923643   124.267508  \n",
       "max       9.935091   298.560117  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets check the statistical summary\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0d9901c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rice           100\n",
       "maize          100\n",
       "jute           100\n",
       "cotton         100\n",
       "coconut        100\n",
       "papaya         100\n",
       "orange         100\n",
       "apple          100\n",
       "muskmelon      100\n",
       "watermelon     100\n",
       "grapes         100\n",
       "mango          100\n",
       "banana         100\n",
       "pomegranate    100\n",
       "lentil         100\n",
       "blackgram      100\n",
       "mungbean       100\n",
       "mothbeans      100\n",
       "pigeonpeas     100\n",
       "kidneybeans    100\n",
       "chickpea       100\n",
       "coffee         100\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets check the Crops present in this Dataset\n",
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b097e395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Ratio of Nitrogen in the Soil :50.551818\n",
      "Average Ratio of Phosphorous  in the Soil :53.362727\n",
      "Average Ratio of Potassium in the Soil :48.149091\n",
      "Average Temperature in Celsius :25.616244\n",
      "Average Relative Humidity :71.481779\n",
      "Average PH Value id the Soil :6.469480\n",
      "Average Rainfall in mm :103.463655\n"
     ]
    }
   ],
   "source": [
    "#Lets check the Summary for all the crops\n",
    "\n",
    "print(\"Average Ratio of Nitrogen in the Soil :{0:2f}\".format(df['N'].mean()))\n",
    "print(\"Average Ratio of Phosphorous  in the Soil :{0:2f}\".format(df['P'].mean()))\n",
    "print(\"Average Ratio of Potassium in the Soil :{0:2f}\".format(df['K'].mean()))\n",
    "print(\"Average Temperature in Celsius :{0:2f}\".format(df['temperature'].mean()))\n",
    "print(\"Average Relative Humidity :{0:2f}\".format(df['humidity'].mean()))\n",
    "print(\"Average PH Value id the Soil :{0:2f}\".format(df['ph'].mean()))\n",
    "print(\"Average Rainfall in mm :{0:2f}\".format(df['rainfall'].mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7c378fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4df0d67e7df4c1e8fcedea8bd0475b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='crops', options=('rice', 'maize', 'jute', 'cotton', 'coconut', 'pa…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets check the Statistics for eacch of the Crops\n",
    "\n",
    "@interact\n",
    "def summary(crops=list(df['label'].value_counts().index)):\n",
    "    x=df[df['label']==crops]\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    print(\"Statistics for Nitrogen\")\n",
    "    print(\"Minium Nitrogen required:\",x['N'].min())\n",
    "    print(\"Avrage Nitrogen required:\",x['N'].mean())\n",
    "    print(\"Maximum Nitrogen required:\",x['N'].max())\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    print(\"Statistics for Phosphorous\")\n",
    "    print(\"Minium Phosphorous required:\",x['P'].min())\n",
    "    print(\"Avrage Phosphorous required:\",x['P'].mean())\n",
    "    print(\"Maximum Phosphorous required:\",x['P'].max())\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    print(\"Statistics for Potassium\")\n",
    "    print(\"Minium Potassium required:\",x['K'].min())\n",
    "    print(\"Avrage Potassium required:\",x['K'].mean())\n",
    "    print(\"Maximum Potassium required:\",x['K'].max())\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    print(\"Statistics for Temperature\")\n",
    "    print(\"Minium Temperature required:\",x['temperature'].min())\n",
    "    print(\"Avrage Temperature required:\",x['temperature'].mean())\n",
    "    print(\"Maximum Temperature required:\",x['temperature'].max())\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    print(\"Statistics for Humidity\")\n",
    "    print(\"Minium Humidity required:\",x['humidity'].min())\n",
    "    print(\"Avrage Humidity required:\",x['humidity'].mean())\n",
    "    print(\"Maximum Humidity required:\",x['humidity'].max())\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    print(\"Statistics for PH\")\n",
    "    print(\"Minium PH required:\",x['ph'].min())\n",
    "    print(\"Avrage PH required:\",x['ph'].mean())\n",
    "    print(\"Maximum PH required:\",x['ph'].max())\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    print(\"Statistics for Rainfall\")\n",
    "    print(\"Minium Rainfall required:\",x['rainfall'].min())\n",
    "    print(\"Avrage Rainfall required:\",x['rainfall'].mean())\n",
    "    print(\"Maximum Rainfall required:\",x['rainfall'].max())\n",
    "    print('-------------------------------------------------------------------------------')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "58844681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c7a835e941d4496b5b424bdd814ff6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='conditions', options=('N', 'P', 'K', 'temperature', 'ph', 'humidit…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets compare the Average kequrement for each crops with average condition\n",
    "@interact\n",
    "def compare(conditions=['N','P','K','temperature','ph','humidity','rainfall']):\n",
    "    print(\"Average Value for \",conditions,\"is:-{0:2f}\".format(df[conditions].mean()))\n",
    "    print(\"-----------------------------------------------------------------\")\n",
    "    print(\"Rice :{0:2f}\".format(df[(df['label']=='rice')][conditions].mean()))\n",
    "    print(\"Pomegranate :{0:2f}\".format(df[(df['label']=='pomegranate')][conditions].mean()))\n",
    "    print(\"Mothbeans :{0:2f}\".format(df[(df['label']=='mothbeans')][conditions].mean()))\n",
    "    print(\"Coconut :{0:2f}\".format(df[(df['label']=='coconut')][conditions].mean()))\n",
    "    print(\"Banana :{0:2f}\".format(df[(df['label']=='banana')][conditions].mean()))\n",
    "    print(\"Jute :{0:2f}\".format(df[(df['label']=='jute')][conditions].mean()))\n",
    "    print(\"Pigeonpeas :{0:2f}\".format(df[(df['label']=='pigeonpeas')][conditions].mean()))\n",
    "    print(\"Papaya :{0:2f}\".format(df[(df['label']=='papaya')][conditions].mean()))\n",
    "    print(\"Muskmelon :{0:2f}\".format(df[(df['label']=='muskmelon')][conditions].mean()))\n",
    "    print(\"Watermelon  :{0:2f}\".format(df[(df['label']=='watermelon')][conditions].mean()))\n",
    "    print(\"Mango :{0:2f}\".format(df[(df['label']=='mango')][conditions].mean()))\n",
    "    print(\"Blackgram :{0:2f}\".format(df[(df['label']=='blackgram')][conditions].mean()))\n",
    "    print(\"Coffee :{0:2f}\".format(df[(df['label']=='coffee')][conditions].mean()))\n",
    "    print(\"Kidneybeans :{0:2f}\".format(df[(df['label']=='kidneybeans')][conditions].mean()))\n",
    "    print(\"Cotton :{0:2f}\".format(df[(df['label']=='cotton')][conditions].mean()))\n",
    "    print(\"Apple :{0:2f}\".format(df[(df['label']=='apple')][conditions].mean()))\n",
    "    print(\"Grapes :{0:2f}\".format(df[(df['label']=='grapes')][conditions].mean()))\n",
    "    print(\"Orange :{0:2f}\".format(df[(df['label']=='orange')][conditions].mean()))\n",
    "    print(\"Chickpea :{0:2f}\".format(df[(df['label']=='chickpea')][conditions].mean()))\n",
    "    print(\"Lentil :{0:2f}\".format(df[(df['label']=='lentil')][conditions].mean()))\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "058ee0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16b7a47c3ff94082ac2488ac4b94bb4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='conditions', options=('N', 'P', 'K', 'temperature', 'ph', 'humidit…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets make this function more intvitive\n",
    "@interact\n",
    "def compare(conditions=['N','P','K','temperature','ph','humidity','rainfall']):\n",
    "    print(\"Crops which require greater than average\",conditions,'\\n')\n",
    "    print(df[df[conditions]>df[conditions].mean()]['label'].unique())\n",
    "    print(\"-------------------------------------------------------------------\")\n",
    "    print(\"Crops which require less than average\",conditions,'\\n')\n",
    "    print(df[df[conditions]<=df[conditions].mean()]['label'].unique())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6dabee03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,4,1)\n",
    "sns.distplot(df['N'],color='aqua')\n",
    "plt.xlabel('Ratio of Nitroagen',fontsize=12)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(2,4,2)\n",
    "sns.distplot(df['P'],color='brown')\n",
    "plt.xlabel('Ratio of Phosphorous ',fontsize=12)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(2,4,3)\n",
    "sns.distplot(df['K'],color='blue')\n",
    "plt.xlabel('Ratio of Potassium',fontsize=12)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(2,4,4)\n",
    "sns.distplot(df['temperature'],color='darkgray')\n",
    "plt.xlabel('Temperature',fontsize=12)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(2,4,5)\n",
    "sns.distplot(df['ph'],color='green')\n",
    "plt.xlabel('ph Level',fontsize=12)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(2,4,6)\n",
    "sns.distplot(df['humidity'],color='pink')\n",
    "plt.xlabel('Humidity',fontsize=12)\n",
    "plt.grid()\n",
    "\n",
    "plt.subplot(2,4,7)\n",
    "sns.distplot(df['rainfall'],color='yellow')\n",
    "plt.xlabel('Rain Fall',fontsize=12)\n",
    "plt.grid()\n",
    "\n",
    "plt.suptitle('Distribution for Agricultural Conditions',fontsize=20)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cba1fe8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some Interesting Patterns\n",
      "----------------------------------------------------------------------------------------------------------------------\n",
      "Crops which requires vary High Ratio of Nitrogen Content is Soil: ['cotton']\n",
      "Crops which requires vary High Ratio of Phosphorous Content is Soil: ['grapes' 'apple']\n",
      "Crops which requires vary High Ratio of potassium Content is Soil: ['grapes' 'apple']\n",
      "Crops which requires vary High Ratio of Rainfall: ['rice' 'papaya' 'coconut']\n",
      "Crops which requires vary High Ratio of Low Temperature: ['grapes']\n",
      "Crops which requires vary High Ratio of High Temperature : ['grapes' 'papaya']\n",
      "Crops which requires vary High Ratio of Low Humidity: ['chickpea' 'kidneybeans']\n",
      "Crops which requires vary High Ratio of Low pH: ['mothbeans']\n",
      "Crops which requires vary High Ratio of High pH: ['mothbeans']\n"
     ]
    }
   ],
   "source": [
    "# Lets find out some Interesting Facts\n",
    "\n",
    "print(\"Some Interesting Patterns\")\n",
    "print(\"----------------------------------------------------------------------------------------------------------------------\")\n",
    "print(\"Crops which requires vary High Ratio of Nitrogen Content is Soil:\",df[df['N']>120]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of Phosphorous Content is Soil:\",df[df['P']>100]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of potassium Content is Soil:\",df[df['K']>200]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of Rainfall:\",df[df['rainfall']>200]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of Low Temperature:\",df[df['temperature']<10]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of High Temperature :\",df[df['temperature']>40]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of Low Humidity:\",df[df['humidity']<20]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of Low pH:\",df[df['ph']<4]['label'].unique())\n",
    "print(\"Crops which requires vary High Ratio of High pH:\",df[df['ph']>9]['label'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64d3a914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summer Crops\n",
      "['pigeonpeas' 'mothbeans' 'blackgram' 'mango' 'grapes' 'orange' 'papaya']\n",
      "---------------------------------------------------------------------------------------------\n",
      "Winter Crops\n",
      "['maize' 'pigeonpeas' 'lentil' 'pomegranate' 'grapes' 'orange']\n",
      "---------------------------------------------------------------------------------------------\n",
      "Rainy Crops\n",
      "['rice' 'papaya' 'coconut']\n",
      "---------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Lets understand which crops can only be Grown in Summer Season,Winter Season and Rainy Season\n",
    "\n",
    "print(\"Summer Crops\")\n",
    "print(df[(df['temperature']>30)&(df['humidity']>50)]['label'].unique())\n",
    "print('---------------------------------------------------------------------------------------------')\n",
    "print(\"Winter Crops\")\n",
    "print(df[(df['temperature']<20)&(df['humidity']>30)]['label'].unique())\n",
    "print('---------------------------------------------------------------------------------------------')\n",
    "print(\"Rainy Crops\")\n",
    "print(df[(df['rainfall']>200)&(df['humidity']>30)]['label'].unique())\n",
    "print('---------------------------------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07d85011",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2200, 7)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "#removing the Labels.column\n",
    "x=df.drop(['label'],axis=1)\n",
    "\n",
    "#selecting all the values of the data\n",
    "x=x.values\n",
    "\n",
    "#checking the shape\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cf8a70e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets determine the optimum nymber of clusters within the dataset\n",
    "\n",
    "plt.rcParams['figure.figsize']=(10,4)\n",
    "wcss=[]\n",
    "for i in range (1,11):\n",
    "    km=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "    km.fit(x)\n",
    "    wcss.append(km.inertia_)\n",
    "    \n",
    "#lets plot the result\n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title(\"The Elbow Method\",fontsize=20)\n",
    "plt.xlabel('No of Clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1d59d03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets check the Results After Applying the k Means Clustering Analysis \n",
      "\n",
      "Crops in First Cluster: ['maize' 'chickpea' 'kidneybeans' 'pigeonpeas' 'mothbeans' 'mungbean'\n",
      " 'blackgram' 'lentil' 'pomegranate' 'mango' 'orange' 'papaya' 'coconut']\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Crops in Secound Cluster: ['maize' 'banana' 'watermelon' 'muskmelon' 'papaya' 'cotton' 'coffee']\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Crops in Third Cluster: ['grapes' 'apple']\n",
      "---------------------------------------------------------------------------------------------------\n",
      "Crops in Forth Cluster: ['rice' 'pigeonpeas' 'papaya' 'coconut' 'jute' 'coffee']\n",
      "---------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Lets implement the k Means algorithm to perform Clustering analysis\n",
    "km=KMeans(n_clusters=4,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "y_means=km.fit_predict(x)\n",
    "\n",
    "#lets find out the Results\n",
    "a=df['label']\n",
    "y_means=pd.DataFrame(y_means)\n",
    "z=pd.concat([y_means,a],axis=1)\n",
    "z=z.rename(columns={0:'cluster'})\n",
    "\n",
    "#lets check the clusters of each Crops\n",
    "print('Lets check the Results After Applying the k Means Clustering Analysis \\n')\n",
    "print('Crops in First Cluster:',z[z['cluster']==0]['label'].unique())\n",
    "print(\"---------------------------------------------------------------------------------------------------\")\n",
    "print('Crops in Secound Cluster:',z[z['cluster']==1]['label'].unique())\n",
    "print(\"---------------------------------------------------------------------------------------------------\")\n",
    "print('Crops in Third Cluster:',z[z['cluster']==2]['label'].unique())\n",
    "print(\"---------------------------------------------------------------------------------------------------\")\n",
    "print('Crops in Forth Cluster:',z[z['cluster']==3]['label'].unique())\n",
    "print(\"---------------------------------------------------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d9a48eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x: (2200, 7)\n",
      "Shape of y: (2200,)\n"
     ]
    }
   ],
   "source": [
    "#lets split the Dataset for Predictive Modelling\n",
    "y=df['label']\n",
    "x=df.drop(['label'],axis=1)\n",
    "\n",
    "print('Shape of x:',x.shape)\n",
    "print('Shape of y:',y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d5afe31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Shape of x train (1760, 7)\n",
      "The Shape of x test (440, 7)\n",
      "The Shape of y train (1760,)\n",
      "The Shape of y test (440,)\n"
     ]
    }
   ],
   "source": [
    "#lets create Training and Testing Sets for Validation of Results\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)\n",
    "\n",
    "print(\"The Shape of x train\",x_train.shape)\n",
    "print(\"The Shape of x test\",x_test.shape)\n",
    "print(\"The Shape of y train\",y_train.shape)\n",
    "print(\"The Shape of y test\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff086451",
   "metadata": {},
   "source": [
    "##  Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b330fac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61336c3a",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f61cd215",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression accuracy=  0.9681818181818181\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\anaconda3\\Anacondanew\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr_classifier = LogisticRegression(random_state = 51, penalty = 'l2')\n",
    "lr_classifier.fit(x_train, y_train)\n",
    "y_pred_lr = lr_classifier.predict(x_test)\n",
    "lr_acc=accuracy_score(y_test, y_pred_lr)\n",
    "print('Logistic Regression accuracy= ',lr_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "06721898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+B65LefzY5OXloNeX4CTFVQQjGl+E7+XhSc8pDf0tDZc7F7gwh8+pptbkywh3ex/D+SND/c3h8s9Ied8uIfjCWkcwojIbuDxT3QQnp/4+aT2ZAZyUw+ea9T3M8Tmpl6lmXT/C511IcInvTr46TzD98khaXwmavmqSLh0N5z9LsJ00z+BX00xT6/B+CsEQ/5rwsxxL0IAo0Kq29YKguXiJYN37kuCE6nuBxkm6g8LP/MvwtT1qW7/C5/43wTq6g2BE5VmSrv6qpe7d1oFw/j4E28TI8H5jgu2gZvtbSdAUnZb0miYEDd5GgqvOHiLYT2zIZd0hy76N7NvBT8PPZWP4/k8DBic9fgm7X6banqCZW0/QlLwJHJHiaxHw65R5u2n5yeypJi/As4ciQdjQQuD7mhRm5fHsqYjIaOBEVa2P86CMQ0T+CTRU1WOK7cXjNv4QicfjsRYJklTPJwguSxBcVXMpaQ5l7WmIyHHAfxEEyjUkeC+OZ/dzmDyeesc3GB6Px2a2ElzqeRVBYNNigubigWKaqke2EJyLM5LgcMmnBAFhz2V6kcdTH/hDJB6Px+PxeCLHX6bq8Xg8Ho8ncuI/RDJaIh0iWf7jW6OU83g8Ho8ncrpyd7bY9EipHpXbd23pKK03X0U5B2PkKx2Z+nkZbZtVM/HSxQDMW9WYn0/uwI4qobQERp24mkM71ylq4Sumv7WSP9w9i0RCOfXcnlwwLDVTqbh6Nnj0NZunZ4NHX7N5ejZ4dLHmyKnXdiY3inKIZMjBmxh9zjczaO5/sx3Dj6pkwiVLuPa7ldz/Zru8tKurld/dOZN7Rn+Xx186idcnLmXRZ5uyv7Ce9Gzw6Gs2T88Gj75m8/Rs8Ohiza5QlAbjiO7badmk+hvzRGDrzsDO5h0ldCjf7cchc2L+7HV03bucLt3LadiohIGndeedKV/k7TVqPRs8+prN07PBo6/ZPD0bPLpYcyxIjlM9krXBEJHeInKziDwkIr8Lbx8QtZFbB67hvqntOeZPPbl3anuu/15dfzsoYO2q7XTo1PSr++06NmXNqu15+4pazwaPvmbz9Gzw6Gs2T88Gjy7W7AoZGwwRuZngVxsFmE7wA0RC8HsSt2R43TARmSEiMyrS/c5jCk/PasnI49bw5pULGXncam6b1DH7i2qhtqtupYCuLWq9ODRN14tD0zW9ODRN14tD0zW9ODRN14tDMw6PkWPgCEa2kzwvJ8ik35U8U0R+Q/DjO/fU9iJVrQAqgJyvInlhTgtuGxj8UOYpvbZw+6v5NRjtOzVl9cqvO8u1q7bTrkPTDK+oXz0bPPqazdOzwaOv2Tw9Gzy6WHMsmNbwkP0QSYLgx3BS6UyaXyrMlw7lVUxfGnxg7y5pSo/Wu7K8onZ6H9Ka5Yu2sGLpVnbtTPD6S0v5zsDOefuKWs8Gj75m8/Rs8OhrNk/PBo8u1uwK2UYwRgBTRORTgl+eBNiL4Cefr8p3ode/2InpS5uxfnspAx7uydVHV3LXSav45esdqEoIjRskuHPQqry0SxuUcPXP+nDzj/+P6mrllLN70HO/lvlajVzPBo++ZvP0bPDoazZPzwaPLtYcCwaOYGSNCheREuBIoCtBCcuA91S1OuMLa/BBWx6Px+NxjHoP2ro7x6Ct2wwK2lLVBPBuvguIuiHoOvqXkeqBb1o8Ho/HYzfGnXSKIb+mWmhCmm3JoHFomq5ng0fT9Wzw6Gs2T88Gjy7WHDkGNhhF/7GzKBLSbEoGjUPTdD0bPJquZ4NHX7N5ejZ4dLFmVyh6gxFFQppNyaBxaJquZ4NH0/Vs8OhrNk/PBo8u1hwLBuZgFL3BiCshzdRk0Dg0TdezwaPpejZ49DWbp2eDRxdrdoWiNxhxJaSZmgwah6bpenFouqYXh6bpenFouqYXh6bpenFo+iTP/Ch6gxFXQtoLc1owaP8tQJAMOntlE2P8mZ4y52s2T88Gj75m8/Rs8OhizbHgG4zdiSshzdRk0Dg0TdezwaPpejZ49DWbp2eDRxdrdoWiX6YaRUKaTcmgcWiarmeDR9P1bPDoazZPzwaPLtYcC6YdsiGHJM9CWc5tkS7AB215PB6Px3TqO8kzcX9uSZ4lPzUoydM04mgGom5afMPiyYeGVeuLbSEruxq0jlSv6ZcLI9Xb3qRnpHoejzUYOIJhRINhYuKabemgpuvZ4NF0vV/dPpd33lxL6zaNeHJC/4K04tCD6GtetXInd962kMrKKkoEBp/TjvMvzO+KsLg8mq5ng0cXa3aBop/kaWrimk3poKbr2eDRdD2AU87szK//3KcgjTj14qi5tFS45sbuPDP+IB4Z05txz6xh4X/yzx8w/XP2254bNceCv4pkd0xNXLMpHdR0PRs8mq4H0Kdfa1q0bFiQRpx6cdTcrn1Deh3QDICyslJ67NOENavzuyIsDo+m69ng0cWaY8HGBkNEeovI8SJSnjL/5CgM2JS4Zmo6qOl6Nng0Xc8G4q55xfIdLJi/jYMOKctbw/TP2W97btTsChkbDBG5BpgAXA3MEZHBSQ+nPTNSRIaJyAwRmTGmYmZGAzYlrpmaDmq6XhyarunZQJw1b9tWzcgbPmfET7tTVl6at47pn7Pf9grXi0PTiu3ZwhGMK4Bvq+qZwLHA/xORa8PH0lpV1QpV7aeq/S4a1jfjAmxKXDM1HdR0PRs8mq5nA3HVXLVLufX6zznp1DYce0JhV7GY/jn7bc+NmmMhogZDRLqLyBsiMk9EPq75zheRNiIyWUQ+Df9m3RizNRilqroFQFUXETQZp4jIb3Kzmh2bEtdMTQc1Xc8Gj6br2UAcNasqd49axN77NOGHQwu7eiQOj6br2eDRxZoNpwq4QVUPAPoDw0XkQOAWYIqq7gdMCe9nJNtlqitFpI+qzgJQ1S0icjrwGHBIAQV8hamJazalg5quZ4NH0/UARt04h5nvrWfjhl0MGfg2lw3fh9PP7mKMXhw1z565lUkT17Hvfk0Zet5cAK68uitHfS8/XdM/Z7/tuVFzHER1yEZVVwArwtubRWQe0BUYTDDIAPAEMBW4OaOnTEmeItINqFLVlbU8drSq/iub2aiTPOPAB215TMAHbRWOD9rymEJ9J3nqQzkmeV7LfwPDkmZVqGpFbc8VkR7AW8DBwBJVbZX02HpVzbhDyDiCoarLMjyWtbmwhagbgk6jom1YVo7yDYsLRP3lbQO+IfB46pewmai1oUgmvHJ0HDBCVTdJHkMkPsmzvvRadKPkrCehvBNoAn2/Ap32EHLcnUjvwaAJ2LqaxPhLYPOK4niMUc8Gj6br2eDR12yeng0eXaw5ciIcLxGRhgTNxVhVfT6cvUpEOqvqChHpDKzOplP0oC3TE9ci00tUkXjtBhJ/PJDE6P7IkcOh/QHoO/eTePgwEn/qiy6YiBzzs+J5jEnPBo+m69ng0ddsnp4NHl2s2WQkGKp4FJinqr9JeugfwMXh7YsJIiwyUvQGw/TEtcj0tqyEFWEmyM4tsGYeNO8KOzZ//ZyGZbVfcF1fHmPSs8Gj6Xo2ePQ1m6dng0cXa46F6HIwjgZ+BAwUkVnhdCpwD3CiiHwKnBjez0jRGwzTE9diSXBrtTd07gvLpwEgA39ByXVLkEMvRN+o+wiGDTWb7tF0PRs8+prN07PBo4s1x0JEDYaqvq2qoqqHqmqfcHpZVStV9XhV3S/8uy6bVtEbDNMT1yJPcGtURsl540hMGvHV6IW+fjuJB/dCZ49Fjryq6B5dTNYzXS8OTdP14tB0TS8OTdP14tC0IsnTQIreYJieuBapXkkDSs4bh340Fua9sNvD+tFTyIFnF9djDHo2eDRdzwaPvmbz9Gzw6GLNsWBhVHjsmJ64FqWeDH4UXTsP/feDX89s862vH+91BqydX1SPcejZ4NF0PRs8+prN07PBo4s1u0LRL1M1PXEtMr29jqbksKHoqtnIlcHJnokpt1LS93Jo1yu4THXDYhITryyex5j0bPBoup4NHn3N5unZ4NHFmmPBwEM2GZM8o8CGJM+o8UFbHo/H4zb1nuT5p9ySPOVKrTdfRR/B2BOJuiHo+ly0DQvA8nN80+LxeDye+DCiwTA9cc3ElLmR4zoy9ZMy2pZVM/HaxQD8fkpbnn2vJW3KqgC4flAlx/TaWhR/9aHpmp4NHn3N5unZ4NHFmiPHwEMkRT/J0/TENVNT5oYcvonRFy/fbf4lR69nwtVLmHD1krybC1NrdlnPBo++ZvP0bPDoYs2uUPQGw/TENVNT5o7ouZ2WzaoL8hGnv7g1XdOzwaOv2Tw9Gzy6WHMs7AmXqYrIk1EaMD1xzYaUuWTGvtuK7z+0NyPHdWTj9vz6Rxtqdk3PBo++ZvP0bPDoYs2ukPEbSET+kTK9CAypuZ/hdcNEZIaIzBhTMTOjAdMT12xImavhh/+1gck3LGTCVYvp0LyKe15un5eODTW7pheHpul6cWi6pheHpul6cWhakeRp4AhGtpM8uwFzgdGAEtjrBzyQ6UXJvzef7TJV0xPXbEiZq6Fd+deHTM49YiNXPtnVGH+mfy6m69ng0ddsnp4NHl2sOQ6Ma3jIfoikH/A+cBuwUVWnAttV9U1VfTMKA6YnrtmQMlfD6k2lX93+59xy9uu4wxh/pn8upuvZ4NHXbJ6eDR5drNkVMo5gqGoCeFBE/h7+XZXtNXXF9MQ1U1Pmrv9bJ6Z/3oz120oZcG9Prj6+kukLmzF/RWMAurbexZ2DVxXNX9yarunZ4NHXbJ6eDR5drDkWDBzBqFOSp4icBhytqjmnNLmY5Bk1PmjL4/F47KK+kzx5LLckTy4zNMlTVV8CXorJiycNcTQDXZ+MtmlZPtQ3LB6Px1M0DBzB8EmeRdAz1ePIFzsy9bMwHXRYkA464vnOLKxsCMDmHaU0b1zNhCuWFM2jy3o2ePQ1m6dng0cXa3aBogdtmZ645lLK3JDDNjH6B99MB/3tkBVMuGIJE65YwqDemzmx95aienRVzwaPvmbz9Gzw6GLNsWDgZapFbzBMT1xzKWXuiL2207Jp7emgqvDK3OacftDmonp0Vc8Gj75m8/Rs8Ohiza5Q9AbD9MQ1nzIXMGNpU9qWVdOjza68Xm96zabr2eDR12yeng0eXaw5FvwIxu6YnrjmU+YCJn6c/+gFmF+z6XpxaJquF4ema3pxaJquF4emFUmeBlL0BsP0xDWfMgdVCZj8STmnHph/g2F6zabr2eDR12yeng0eXaw5FvwIxu6YnrjmU+bgnYXN2KftTjq1qDLGo2t6Nnj0NZunZ4NHF2t2haJfpmp64ppLKXPXv9CJ6YubsX57KQMe6snVAyo5t88mXp7bnNMKGL2I0qOrejZ49DWbp2eDRxdrjgUDD9nUKckzH3ySp5n4oC2Px+OJj3pP8vxrjkmePzI0yTMuTA9E2RNDYLI1BHXV6/r07g3LyPEdmbogDO4avvir+X+d1oox01vRoEQ5Zr+t3DRo7e7+fpi9YSn2e1jfejZ49DWbp2eDRxdrjhwDRzCKfg6G6YEoLobARBbc1WcToy/6ZnDXuwubMmV+GS/+ZDEvDV/M5UetL6pHW/Rs8OhrNk/PBo8u1uwKRW8wTA9EcTEEJrLgrh67B3c9/V4rhn13PY0aBKN5bctrD/aqL4+26Nng0ddsnp4NHl2sORb8VSS7Y3ogioshMHGGyiyqbMiMJU0595HuXPR4N2Yvb2yER9P1bPDoazZPzwaPLtYcC7Y1GCLyXyLSIrzdVETuEJEXReReEYnkFFrTA1FcDIGJM1SmOiFs2l7Csz9eyk0nrmXE37vUurxsmF6zX28K14tD0zW9ODRN14tD0wdt5Ue2EYzHgG3h7d8BLYF7w3mPp3uRiAwTkRkiMmNMxcyMCzA9EMXFEJg4Q2U6tqjixAO2IAKHdvuSElHWbystukfT9Wzw6Gs2T88Gjy7WHAu2jWAAJapak67UT1VHqOrbqnoHsE+6F6lqhar2U9V+Fw3rm3EBpgeiuBgCE2eozAm9t/DuwmYALFzbkF3VQutmdT8Pw/Sa/XrjazZBzwaPLtbsCtkuU50jIpeq6uPAhyLST1VniMj+QH6/epWC6YEoLobARBbc9Vwnpi9qxvptpQx4oCdXH1fJ2X03cuuETpz+x71pWKrcc+bKvIYaTa05Lj0bPPqazdOzwaOLNceCgYdsMgZthedZ/A74HrAWOBxYGk7XqOqH2Rbgg7bcoLYcjELIJQfD4/F4TKXeg7aeyTFo6weGBG2p6kbgEhFpTnBIpAGwTFVX1Yc5j8fj8Xg8OWDgCEZOSZ6quhnIOlqRL6YnrrmYMlfnZNCIkze7jq4lGfSVjkz9vIy2zaqZeGmQDDpvVWN+PrkDO6qE0hIYdeJqDu38Ze0efxxtemk2/HrjazZBzwaPLtbsAkXPwTA9cc3FlDlTax5y8CZGn/PNZND732zH8KMqmXDJEq79biX3v9muaP7i1LPBo6/ZPD0bPLpYcyxYeBVJ7JieuOZiypypNR/RfTstm3zzihMR2LozWI037yihQ3l+Pynvyntok54NHk3Xs8GjizW7QtEbDNMT11xMmbOh5hpuHbiG+6a255g/9eTeqe25/nu7/3BaMfzZ8B6armeDR9P1bPDoYs2xENEIhog8JiKrRWRO0rxRIrJcRGaF06m5WCp6g2F64pqLKXM21FzD07NaMvK4Nbx55UJGHrea2yZ1zEvHxffQdL04NF3Ti0PTdL04NK1I8ozuEMlfgJNrmf+gqvYJp5dzESp6g2F64pqLKXM21FzDC3NaMGj/LQCc0msLs1c2McKfDe+h6Xo2eDRdzwaPLtZsMqr6FrAuCq2iNximJ665mDJnQ801dCivYvrSYEN/d0lTerTOL//NxffQdD0bPJquZ4NHF2suJsk/5RFOw3J86VUiMjs8hNI6lxfkdJlqnJieuOZiypypNV//YiemL23G+u2lDHi4J1cfXcldJ63il693oCohNG6Q4M5B+UW0uPIe2qRng0fT9Wzw6GLNsZDjIRtVrQAq6qj+MHAXoOHfB4DLslrKlOQZBT7J05MPteVgFEq2HAyPx+OJinpP8hyXY5Ln2dmTPEWkBzBRVQ+uy2OpFH0Ew+OpjTiagc73R9u0rPipb1g8Ho8hxNjOiEhnVV0R3j0LmJPp+TUU/RwMCBLShp40iYtOfIWnKubv8XpxaJquF4dmwXrNuyHnv45cNhe5dA4cfk0wf/9zkEvnIDdWQ8dvF89fPWiarheHpmt6cWiarheHZhweIyW6y1SfBv4N9BKRZSJyOXCfiHwkIrOB44DrcrFU9AbD9MQ1F1PmnKk5UYW+cQP62IHomP5I3+HQ9gBYOwcdPwSWvlVcfzFrmq5ng0fT9Wzw6GLNJqOqP1TVzqraUFW7qeqjqvojVT1EVQ9V1TOSRjMyUvQGw/TENRdT5pypeetKWD0zuL1rC1TOg/KusG4+rF+Qt7fI/MWsabqeDR5N17PBo4s1x4JtUeEi0khEhorICeH9C0TkDyIyXEQaRmHA9MQ1F1PmXKyZFntDx76wYlr+Gkm4+B76ms3Ts8GjizW7QrYRjMeB04BrReSvwLnANOAIYHS6FyVfZzumYmbGBZieuOZiypxzNTcsQwaPQ18fATs3528qCefewxj04tB0TS8OTdP14tB0LMkzMrJdRXKIqh4qIg2A5UAXVa0WkTFk+Pn25Otss12manrimospc07VXNIgaC7mjYVPX8jbT2z+YtQ0Xc8Gj6br2eDRxZpdIdsIRomINAKaA82AmmSRxkAkh0hMT1xzMWXOpZrl5EeDcy9mPJi3lzj9xalpup4NHk3Xs8GjizXHgoUjGI8C84FS4Dbg7yLyOdAfeCYKA6YnrrmYMudMzV2PRg4aiq6ZjVwcHMrTt26FBo2R438PTdsjZ78Eq2ehz9X22z8x+4tZ03Q9GzyarmeDRxdrdoWsSZ4i0gVAVb8QkVbACcASVZ2eywJ8kqfHFHzQlsfjqS/qPcnzxRyTPL+fPckzKrImearqF0m3NwDPxWnI44mLqBuCLr+LtmH54lrfsHg8nj0HI6LCp7+1kj/cPYtEQjn13J5cMKz3Hq1ng0dfc931VmxuwM1TOrF2WyklAucduJGhh21gw5clXP9aZ5ZvbkjX5rt4cNAKWjZJFMWjbXo2eDRdzwaPLtYcOaZd1YIBQVumJ665mDLna85Pr7REufnoNbx8wWKeOXsJY+e04rN1jXjkgzb077aNVy9cRP9u23hkZpuiebRJzwaPpuvZ4NHFmmPBwJM8i95gmJ645mLKnK85P70OZdUc1H4HAOWNlH1b72TV1gZMWVTOmb2CndGZvTbxz4XlRfNok54NHk3Xs8GjizW7QtEbDNMT11xMmfM1F663bFMD5q1tzGEdv6RyWykdyqqBoAlZt73UCI+m69ng0XQ9Gzy6WHMs+BGM3TE9cc3FlDlfc2F6W3cJ17zahZFHr6G8UX7nWtSGyTXHoReHpmt6cWiarheHphVJngZS9AbD9MQ1F1PmfM356+2qhmsmdeH7+21i0L5bAGjbrJrVW4NRi9VbS2nTtLqoHm3Rs8Gj6Xo2eHSx5ljwIxi7Y3rimospc77m/PRU4fY3OrFv651c2mfDV/MH9tjC+E9aADD+kxYc32NL0TzapGeDR9P1bPDoYs2uUPTLVE1PXHMxZc7XnJ/eByubMGFBC/Zvs4Mz/7YXANf1r+SKw9dx3atdGDevJZ3Lq/jtSfmdHGZizXHq2eDRdD0bPLpYcywYeMgma5JnofgkT8+eig/a8ng86aj3JM9JOSZ5nmxQkqcLNKxaH6nergatI9XzmEnUDUHXp6NtWJb/0DcsHo8zGDiCYUSDYXri2q9un8s7b66ldZtGPDmhf0FacXk0Xc8GjybqjRzfkakLymhbVs3E4Yu/mv/Xaa0YM70VDUqUY/bbyk2D1hbNY5x6Nng0Xc8Gjy7W7AJFP8nThsS1U87szK//3KcgjWRMr9nFZD1T9Yb02cToi5Z/Y967C5syZX4ZL/5kMS8NX8zlR+U3AmdqzTZ5NF3PBo8u1hwLtl1FIiItReQeEZkvIpXhNC+c1yoKAzYkrvXp15oWLRsWpJGM6TW7mKxnqt4RPbbTMuWy1qffa8Ww766nUYPgkGvb8vwuezW1Zps8mq5ng0cXa44F2xoM4FlgPXCsqrZV1bbAceG8v0dhwMXENdNrdjFZz3S9ZBZVNmTGkqac+0h3Lnq8G7OXNzbCo19vzNOzwaOLNbtCtgajh6req6ora2ao6kpVvRfYK92LRGSYiMwQkRljKmZmXICLiWum1+xisp7peslUJ4RN20t49sdLuenEtYz4e5dal5cNG2o23aPpenFomq4Xh6YN3ysmku0kz8UichPwhKquAhCRjsAlwNJ0L1LVCqACsl+m6mLimuk1u5isZ7peMh1bVHHiAVsQgUO7fUmJKOu3ldKmrG6HSmyo2XSPpuvZ4NHFmmPBwIYn2wjG+UBb4E0RWSci64CpQBvg3CgMuJi4ZnrNLibrma6XzAm9t/DuwmYALFzbkF3VQutmdT8Pw4aaTfdoup4NHl2s2RUyjmCo6nrg5nD6BiJyKfB4oQZsSFwbdeMcZr63no0bdjFk4NtcNnwfTj+7izEeTdezwaOpetc/14npi5qxflspAx7oydXHVXJ2343cOqETp/9xbxqWKvecuTKv4VpTa7bJo+l6Nnh0seZYMHAEI+8kTxFZoqppz8OowYYkTx+05TEBH7Tl8ew51HuS5+s5JnkONCTJU0Rmp3sI6Bi9neLgG4LCafrlwkj1tjfpGameDUTdEETdsIBvWjweT+5kO8mzI3ASwWWpyQjwTlQmTE9cczFlLmq9VSt3cudtC6msrKJEYPA57Tj/wsJ6VNNrNnG9sS0ZNA5N1/Rs8OhizZFj4GUt2U7ynAiUq+rilGkRwcmeBWN64pqLKXNx1FxaKlxzY3eeGX8Qj4zpzbhn1rDwP/lfR256zaauNzYlg8ah6ZqeDR5drDkWbAvaUtXLVfXtNI9dEIUB0xPXXEyZi6Pmdu0b0uuA4MqHsrJSeuzThDWrdxnj0XS9qDRtSgaNQ9M1PRs8ulhzLIjkNtUjRf8tEtMT11xMmYs7tW7F8h0smL+Ngw4py1vD9JptWG9qMDUZNA5N1/Rs8OhizbFg2whGfWB64pqLKXNxptZt21bNyBs+Z8RPu1NWXpq3juk127De1GBqMmgcmq7pxaFpul4cmnYkeZrXYRS9wTA9cc3FlLm4Uuuqdim3Xv85J53ahmNPKOzKHdNrtmG9qSFdMqgJ/kz/XEzXs8GjizXHgnn9RfEbDNMT11xMmYujZlXl7lGL2HufJvxwaOFXOJtesw3rTQ2mJoPGoemang0eXaw5FgxsMLJdpho7pieuuZgyF0fNs2duZdLEdey7X1OGnjcXgCuv7spR38tP1/SaTV1vbEoGjUPTNT0bPLpYczwYd8wm/yTPXLEhydNTOD5oyzx80JbHUzzqPcnznUa5fdcetdOMJE+PJ1d8Q2AecTQDne+PtmlZ8VPfsHg8eypFPwcDgoS0oSdN4qITX+Gpivl7vF4cmqbrxaHpml4cmgXrNe+GnP86ctlc5NI5cPg1wfz9z0EunYPcWA0dv11cj47rxaFpul4cmnF4jJSIcjBE5DERWS0ic5LmtRGRySLyafg3p7P0i95gmJ645mLKnK/ZPD1jPSaq0DduQB87EB3TH+k7HNoeAGvnoOOHwNK38vYXmUeH9Wzw6GLNsRDdSZ5/AU5OmXcLMEVV9wOmhPezkrHBEJEWIvIrEfmriFyQ8tj/5mQ1C6YnrrmYMudrNk/PWI9bV8LqmcHtXVugch6Ud4V182H9gry9RerRYT0bPLpYs8mo6lvAupTZg4EnwttPAGfmopVtBONxgp5nHPADERknIjUxf/1zcpsF0xPXXEyZ8zWbp2eFxxZ7Q8e+sGJa/hopmF6z6Xo2eHSx5ljI8RCJiAwTkRlJ07Ac1Duq6gqA8G+HXCxlazD2VdVbVHW8qp4BfAC8LiJtM9f5dQFjKmZmXIDpiWsupsz5ms3Ti0MzUr2GZcjgcejrI2Dn5vxNpWB0zRboxaFpul4cmlYkeebYYKhqhar2S5oq4rKU7SqSxiJSoqoJAFW9W0SWAW8B5eleFBqugOyXqZqeuOZiypyv2Tw9oz2WNAiai3lj4dMX8vZTG8bWbImeDR5drDkW4m14VolIZ1VdISKdgdW5vCjbCMaLwMDkGar6BHADsDMvmymYnrjmYsqcr9k8PZM9ysmPBudezHgwby9xe3RVzwaPLtYcC/Emef4DuDi8fTEwIZcXZRzBUNWb0syfJCKRXBBveuKaiylzvmbz9Iz12PVo5KCh6JrZyMXB4VB961Zo0Bg5/vfQtD1y9kuwehb6XOqJ6fXk0WE9Gzy6WHMsRHTMRkSeBo4F2oVHLH4O3AM8KyKXA0uAc3PSyjfJU0SWqOpe2Z7nkzw9nj0HH7Tl8eRGvSd5vt8st+/ab28zI8lTRGanewgo/BerDMHHXHs8uRF1Q9BpVLQNy8pRvmHxOEqJaWedZj/JsyNwErA+Zb4A70RlYvpbK/nD3bNIJJRTz+3JBcN6G6O3auVO7rxtIZWVVZQIDD6nHedfWHhvZXLNcejZ4NF0PRs8RqLXohslZz0J5Z1AE+j7Fei0h5Dj7kR6DwZNwNbVJMZfAptXFMejRXo2eHSxZhfIdpLnRKBcVRenTIuAqVEYMD1xrbRUuObG7jwz/iAeGdObcc+sYeF/Crv+2fSaXUzWM13PBo+R6SWqSLx2A4k/HkhidH/kyOHQ/gD0nftJPHwYiT/1RRdMRI75WfE8WqJng0cXa46FiKLCoyRjg6Gql6vq22keu6C2+XXF9MS1du0b0uuAZgCUlZXSY58mrFm9K2+9ODyarmeDR9P1bPAYmd6WlbAizM/ZuQXWzIPmXWFHUr5Gw7Lawwnqy6MlejZ4dLHmWIj3KpK8KPpvkdiUuLZi+Q4WzN/GQYeUFaRjes0uJuuZrmeDx1i2vVZ7Q+e+sDxIB5WBv6DkuiXIoReib9R9BMP0mv1640bN8WBeh1H0BsOWxLVt26oZecPnjPhpd8rKSwvSMr1mF5P1TNeLQ9N0PRqVUXLeOBKTRnw1eqGv307iwb3Q2WORI68qukfT9eLQNF0vDk07kjxznOqRojcYNiSuVe1Sbr3+c046tQ3HntC6IC0wv2YXk/VM17PBY6R6JQ0oOW8c+tFYmLd7Oqh+9BRy4NnF9WiBng0eXaw5Fmw7B6M+MD1xTVW5e9Qi9t6nCT8cGs2VuabX7GKynul6NniMUk8GP4qunYf+OykdtM23vn681xmwdn5RPdqgZ4NHF2uOBQNHMLJdpho7pieuzZ65lUkT17Hvfk0Zet5cAK68uitHfc8cj6br2eDRdD0bPEamt9fRlBw2FF01G7kyONkzMeVWSvpeDu16BZepblhMYuKVxfNoiZ4NHl2sORaMO2ZTQJJnrtiQ5OmDtjye4uCDtjx7KvWe5Plxy9y+aw/aaEaSpyv4hsDjKQ5RNwRdn4y2YQFYPtQ3LR4LMG8Aw4wGw/TENRdT5nzN5unZ4NHEmke+2JGpn5XRtqyaicMWAzDi+c4srGwIwOYdpTRvXM2EK5YUxV/cejZ4dLHmyDHwEEmdT/IUkQ5RGjA9cc3FlDlfs3l6Nng0teYhh21i9A+Wf2Peb4esYMIVS5hwxRIG9d7Mib23FM1fnHo2eHSx5liw7SoSEWmTMrUFpotIaxFpE4UB0xPXXEyZ8zWbp2eDR1NrPmKv7bRsWl3rY6rwytzmnH7Q5lofrw9/cerZ4NHFmmPBwKtIso1grAXeT5pmAF2BD8LbBWN64pqLKXO+ZvP0bPBoQ82pzFjalLZl1fRok1/8v4vvoel6tniMHvM6jGwNxk3AJ8AZqtpTVXsCy8Lb+6R7kYgME5EZIjJjTMXMjAswPXHNxZQ5X7N5enFomq4Xl2YyEz/Of/QC3HwPTdeLQ9MneeZHxpM8VfXXIvIM8KCILAV+DmS9FEZVK4AKyH6ZqumJay6mzPmazdOzwaMNNSdTlYDJn5Tz/GX5ndwJbr6HpuvZ4jFyTGt4yOEkT1VdpqrnAm8Ak4FmURowPXHNxZQ5X7N5ejZ4tKHmZN5Z2Ix92u6kU4sqY/zZ8B6armeLx8gx8CTPnC9TVdUXReSfwL4AInKpqj5eqAHTE9dcTJnzNZunZ4NHU2u+/oVOTF/cjPXbSxnwUE+uHlDJuX028fLc5px2YP6HR6LyF6eeDR5drDkWjDtmU0CSp4gsUdW9sj3PhiRPj8ezZ+CDtjymUO9Jnv9pl9t37b5rzUjyFJHZ6R4CovnlL8wPRHExBMbXbJ6eDR6LXXMuzUBdPe4WZ96iGyVnPQnlnUAT6PsV6LSHkOPuRHoPDn4rZetqEuMvgc0rdtPLll5a7PdwT9CzxWOkmDeAkfUcjI7AUOD7tUyVURgwPRDFxRAYX7N5ejZ4dKbmRBWJ124g8ccDSYzujxw5HNofgL5zP4mHDyPxp77ogonIMT8rjr+YNU3Xs8Vj5Bh4Dka2BmMiUK6qi1OmRcDUKAyYHojiYgiMr9k8PRs8OlPzlpWwIrz8fucWWDMPmneFHUnnczQsq/3axvrwF7Om6Xq2eHSBjA2Gql6uqm+neeyCKAyYHojiYgiMr9k8PRs8ulgzrfaGzn1h+TQAZOAvKLluCXLohegbdR/BcPE9dLHmWCiR3Kb6tFSvS6sF0wNRXAyB8TWbpxeHpul6cWhGqteojJLzxpGYNOKr0Qt9/XYSD+6Fzh6LHHlVcf3FpGm6XhyaVgRtGUjRGwzTA1FcDIHxNZunZ4NHp2ouaUDJeePQj8bCvBd2e1g/ego58Ozi+YtR03Q9WzxGjoXnYMSO6YEoLobA+JrN07PBo0s1y+BH0bXz0H8/+PXMNt/6+vFeZ8Da+UXzF6em6Xq2eIwc26LC6wPTA1FcDIHxNZunZ4NHZ2re62hKDhuKrpqNXBmc7JmYcislfS+Hdr2Cy1Q3LCYx8cri+ItZ03Q9WzxGj3nHbPIO2soVH7Tl8XhsZrccjALJloPh2TOo96Ct5Z1y+67tutKMoC2Px+NxnagbAt+weOLBvBEMIxoM0xPXXEyZ8zWbp2eDR19zHnoFJoPWi0fL9GzxGCnm9RfFP8nT9MQ1F1PmfM3m6dng0ddsXjJoZB4t0rPFY+TsCVeRiEjbKA2YnrjmYsqcr9k8PRs8+prNSwaNzKNFerZ4dIGMDYaI3CMi7cLb/UTkc2CaiCwWkWOiMGB64pqLKXO+ZvP0bPDoazYvGTQOj6br2eIxcgy8TDXbCMZpqro2vH0/cL6qfgs4EXgg3YtEZJiIzBCRGWMqZmZcgOmJay6mzPmazdOLQ9N0vTg0jdaLIRk0co8W6MWhaUWSp5TkNtUj2ZbWUERqTgRtqqrvAajqAqBxuhepaoWq9lPVfhcN65txAaYnrrmYMudrNk/PBo++ZvOSQSP1aImeLR6jRqUkp6k+yba0PwIvi8hAYJKI/FZEBojIHcCsKAyYnrjmYsqcr9k8PRs8+prNSwaN0qMterZ4jB7zjpFkvExVVX8vIh8BPwH2D5+/PzAeuCsKA6YnrrmYMudrNk/PBo++ZvOSQSPzaJGeLR6jRnO8ZqM+W4y8kzxF5FJVfTzb83ySp8fj8XyND9pyg/pO8kys2Sen79qS9p9n9SUii4DNQDVQpar98vFUSNDWHUDWBsPj8XjS0bBqfaR6uxq0jlQvDqJuCLr8LtqG5YtrfcNiJ5GfX3Fc0kUeeZGxwRCR2ekeAjoWsuBkTE9cczFlztdsnp4NHqPW+9Xtc3nnzbW0btOIJyf0L0grLo8m6q3Y3ICbp3Ri7bZSSgTOO3AjQw/bwIYvS7j+tc4s39yQrs138eCgFbRskiiKxzj1bPEYJSqlxbawG9lano7AUOD7tUyVURgwPXHNxZQ5X7N5ejZ4jKPmU87szK//3KcgjWRMrzkqvdIS5eaj1/DyBYt55uwljJ3Tis/WNeKRD9rQv9s2Xr1wEf27beORmW2K5jEuPVs8Ro0iOU05y8FrIvK+iAzL11O2BmMiUK6qi1OmRcDUfBeajOmJay6mzPmazdOzwWMcNffp15oWLRsWpJGM6TVHpdehrJqD2u8AoLyRsm/rnaza2oApi8o5s1fwxXhmr038c2F50TzGpWeLx8jJMQcjOacqnGprII5W1cOBU4DhIjIgH0sZGwxVvVxV307z2AX5LDAV0xPXXEyZ8zWbp2eDRxvSDk2vOY73cNmmBsxb25jDOn5J5bZSOpRVA0ETsm573YfVbajZBo9Rk+sIRnJOVThV7Kal+kX4dzXwAnBkPp6K/mNnpieuuZgy52s2Ty8OTdP14sD0mqPW27pLuObVLow8eg3ljep+rkVtmF5zHJo2rNvB13kuU2ZEpExEmtfcBgYBc/JxVPSfazc9cc3FlDlfs3l6Nni0Ie3Q9Jqj1NtVDddM6sL399vEoH23ANC2WTWrtwajGKu3ltKmaXVRPcahZ4vHqInwJM+OwAsSdFANgKdUdVI+QkUfwTA9cc3FlDlfs3l6Nni0Ie3Q9Jqj0lOF29/oxL6td3Jpnw1fzR/YYwvjP2kBwPhPWnB8jy1F8xiXni0eoyeaJE9V/VxVDwung1T17nwdFX0Ew/TENRdT5nzN5unZ4DGOmkfdOIeZ761n44ZdDBn4NpcN34fTz+5ijEdT9T5Y2YQJC1qwf5sdnPm3vQC4rn8lVxy+jute7cK4eS3pXF7Fb0+q+4mKptZsm8eoqe/fGcmFvJM8c8UneXo8nnS4GLQVNT5oy0zqO8lzx/p+OX3XNm49o958FX0Ew+PxuIuLDUHURN0QdP5ltA0LwIpbfdMSN7n+Fkl9YkSDYXrimospc75m8/Rs8OhrNk8vEs3m3ZAznoSyTqAJdFYFvPcQMvA+2O/7UL0T1v8HnXgp7NhY//7qQdP0JE8DL2spfstjeuKaiylzvmbz9Gzw6Gs2Ty8yzUQV+s8b0IoD0Sf6I4cPh3YHoAsnoxUHo6MPg3ULkKNGFsdfzJp2JHmW5jTVJxkbDBHpJyJviMgYEekuIpNFZKOIvCcifaMwYHrimospc75m8/Rs8OhrNk8vMs2tK2FV8FPy7NwClfOgvCssnAwaXOqqy9+F5t2K4y9mzT0pybM+yba0/wXuA14C3gH+rKotgVvCxwrG9MQ1F1PmfM3m6dng0ddsnl4smi33ho594Ytp35gth12G/ueV4vuLQXNPSvKsT7I1GA1V9RVVfRpQVX2O4MYUoEm6FyVnnY+pmJlxAaYnrrmYMudrNk8vDk3T9eLQdE0vcs2GZciQceg/R8DOzV/PP+pWSFTBx2OL6y8mTSuSPA0cwch2kueXIjIIaAmoiJypquNF5BggbQRcmG1eAdkvUzU9cc3FlDlfs3l6Nnj0NZunF6lmSQPk7HHox2Phkxe+nn/IUORbp6NPHV9cfzFq2pDkmUuIVn2TrZ25ErgBuAw4CThORDYQHB65JgoDpieuuZgy52s2T88Gj75m8/Si1JTTHoW182D6g1/P3Ock5Ds3o8+dAVX5HTIwueY4PUaNUpLTVJ9kHMFQ1Q8JGosarg0nRORSgvMyCsL0xDUXU+Z8zebp2eDR12yeXmSa3Y5GDhmKrp6NXB4c9taptyInPgQNGiM/nBw8b/m76KSf1L+/mDXtSPKs3ytEciHvJE8RWaKqe2V7nk/y9Hg8HnvwQVvRUN9Jnls2D8rpu7a8+WtmJHmKyOx0DxH84prH4/F49iDiaAY6jYq2aVk5yr2GJRsm/hZJtpM8OxIcIkn9wQAhgsMjNZieuOZiypyv2Tw9Gzz6ms3TM9Jji26UnPUklIfJoO9XoNMeQo67E+k9GDQBW1eTGH8JbF5RHI8x60WPfSd5TgTKVXVxyrQImBqFAdMT11xMmfM1m6dng0dfs3l6xnpMVJF47QYSfzyQxOj+yJHDof0B6Dv3k3j4MBJ/6osumIgc87PieYxRLx5Kcpzq11FaVPVyVX07zWMXRGHA9MQ1F1PmfM3m6dng0ddsnp6xHreshBVJyaBr5kHzrrAjKVujYVntART15TFGvThQKc1pqk+KftDG9MQ1F1PmfM3m6dng0ddsnp4VHlvtDZ37wvIgGVQG/oKS65Ygh16IvpHfCIbxNceAiuQ01SdFbzBMT1xzMWXO12yeXhyapuvFoemaXhyakeo1KqPkvHEkJo34avRCX7+dxIN7obPHIkdeVXyPMejFg2WHSOoD0xPXXEyZ8zWbp2eDR1+zeXpGeyxpQMl549CPxsK8F3Z7WD96Cjnw7OJ6jEkvDmz8LZLYMT1xzcWUOV+zeXo2ePQ1m6dnskcZ/Ci6dh7676Rk0Dbf+vrxXmfA2vlF9RiXXixY+FsksWN64pqLKXO+ZvP0bPDoazZPz1iPex1NyWFD0VWzkSuDkz0TU26lpO/l0K5XcJnqhsUkJl5ZPI8x6sWDccds8k/yzBWf5OnxeDxu42LQVn0nea7/8gc5fde2bvKMGUmeHs+eRNsNr0eqV9lqYKR6Hs+eStQNQef7o21YVvzU/IYlG/X9Q2a5YISj6W+tZOhJk7joxFd4qiK/Y2426cWhabpeHJpxeKyuVs770Uauun5z9idnwcX30Ndsnl4cmkbqNe+GnP86ctlc5NI5cHj4g9/7n4NcOge5sRo6fru4HmNFcpzqj4wNhoi0FJF7RGS+iFSG07xwXqsoDJieuOZMsl6MerZ4BBj7ty/Zp0fhYTQuvoe+ZvP0bPAYmV6iCn3jBvSxA9Ex/ZG+w6HtAbB2Djp+CCx9q/geY0SlJKepPsm2tGcJfofkWFVtq6ptgePCeX+PwoDpiWvOJOvFqGeLx1WrEvzfv3Zx1uDGBenE5c/099DXbJ6eDR4j09u6ElaH6aC7tkDlPCjvCuvmw/oFefuL1GOsWDaCAfRQ1XtVdWXNDFVdqar3All/qj0XTE9cczFZz8WaAe57cCvXXdWMkgi2QRffQ1+zeXo2eIwlJbPF3tCxL6yYVphOiA1JniZeppptaYtF5CYR+eqn2UWko4jcDCxN9yIRGSYiM0RkxpiKmRkXYHrimnPJejHoxaEZtd6bb++kTZsSDjwgmvOeXXwPfc3m6cWhaboeDcuQwePQ10fAzsLPpQI7kjyV0pym+iTb3vR84BbgzbDJUGAV8A/gvHQvUtUKoAKyX6ZqeuKaU8l6MenZ4HHWh1VMfWsnb7+zix07lK1blZE/38Kv7ig3wl8cmqbr2eDRdD0bPEaqV9IgaC7mjYVPd08HzRcrkjxN63jI/muq64HHgauA7qraRlUPUNWbgSOjMGB64ppLyXpx6dng8drhzZg8sTWvjG/Fvb8o54h+DfNuLuLwF4em6Xo2eDRdzwaPUerJyY8G517MeDD7k4vkMT7M+y2SjCMYInINMByYB4wWkWtVdUL48C+BSYUaMD1xzZlkvRj1bPEYJS6+h75m8/Rs8BiZXtejkYOGomtmIxcHh+b1rVuhQWPk+N9D0/bI2S/B6lnocycXx2OsmDeCkTHJU0Q+Ar6jqltEpAfwHPBXVf2diMxU1b7ZFuCTPD2m4IO2PJ49AxuCtuo7yXN11U9y+q7t0OBhY5I8S1V1C4CqLhKRY4HnRGRvTGyXPJ4M+IbA49kziLoh6PpktA0LAEPvjl4zA/V9AmcuZGswVopIH1WdBRCOZJwOPAYcEpWJ6W+t5A93zyKRUE49tycXDOu9R+vZ4NHXbJ6eDR59zebp2eDRxJpHvtiRqZ+V0basmonDFgMw4vnOLKxsCMDmHaU0b1zNhCuWFOw1Emw7yRMYCqxMnqGqVao6FBgQhQFjU+Fi0rPBo6/ZPD0bPPqazdOzwaOpNQ85bBOjf7D8G/N+O2QFE65YwoQrljCo92ZO7L2lIJ9RopTkNNUn2a4iWZYcspXy2L+iMGBsKlxMejZ49DWbp2eDR1+zeXo2eDS15iP22k7LptW1PqYKr8xtzukHRZOzEQ32JXnGjumpcD5Zz9dsgp4NHn3N5unZ4NGGmlOZsbQpbcuq6dFmV2SaBWNgkmfRf67d9FQ4n6xXuF4cmq7pxaFpul4cmq7pxaFpul5cmslM/Ni00QtQA6+7KPoIhtGpcDHo2eDR12yeng0efc3m6dng0Yaak6lKwORPyjn1QMMaDCnNaapPit5gmJwKF4eeDR59zebp2eDR12yeng0ebag5mXcWNmOftjvp1KIqEr3oiC7JU0ROFpFPROQzEbklX0dFP0RibCpcTHo2ePQ1m6dng0dfs3l6Nng0tebrX+jE9MXNWL+9lAEP9eTqAZWc22cTL89tzmmGjV5AdL9FIiKlwB+BE4FlwHsi8g9VnVtnrUxJnlHgkzw9Ho/HYzLxBG1pvZ4Uket3bbaEURH5DjBKVU8K748EUNVf1dmUqhoxAcNM1zRdzwaPpuvZ4NHXbJ6eDR59zebpFWMChgEzkqZhKY+fA4xOuv8j4A/5LKvo52AkMcwCTdP14tB0TS8OTdP14tB0TS8OTdP14tB0Ta/eUdUKVe2XNFWkPKW2EY68jkSY1GB4PB6Px+MpLsuA7kn3uwF5JZ/5BsPj8Xg8Hk8N7wH7iUhPEWkE/AD4Rz5CRb+KJInUYRoTNU3Xi0PTNb04NE3Xi0PTNb04NE3Xi0PTNT3jUNUqEbkKeBUoBR5T1Y/z0Yr9KhKPx+PxeDzu4Q+ReDwej8fjiRzfYHg8Ho/H44kcIxqMqGJJk/QeE5HVIjInAq3uIvKGiMwTkY9F5NoINJuIyHQR+TDUvKNQzVC3VERmisjECLQWichHIjJLRGZE5K+ViDwnIvPD9/M7BWj1Cr3VTJtEZESB/q4LP485IvK0iDQpUO/aUOvjfL3Vti6LSBsRmSwin4Z/Wxeod27oMSEi/SLyeH/4Oc8WkRdEpFWBeneFWrNE5DUR6VKIXtJjN4qIiki7Av2NEpHlSevjqbnqZfIoIleH+8aPReS+Aj3+LcnfIhGZVaBeHxF5t2YfISJH5qqXQfMwEfl3uO95UURa1EGv1n11vttLBr2CthenMCD0oxT4D7AP0Aj4EDiwQM0BwOHAnAj8dQYOD283BxZE4E+A8vB2Q2Aa0D8Cr9cDTwETI9BaBLSL+LN+AvhxeLsR0CrCdWglsHcBGl2BhUDT8P6zwCUF6B0MzAGaEZxM/U9gvzx0dluXgfuAW8LbtwD3Fqh3ANALmAr0i8jjIKBBePveCDy2SLp9DfCnQvTC+d0JTmRbXJd1PY2/UcCNBawvtWkeF643jcP7HQqtOenxB4CfFejvNeCU8PapwNQIan4POCa8fRlwVx30at1X57u9ZNAraHtxaTJhBONI4DNV/VxVdwLPAIMLEVTVt4B1UZhT1RWq+kF4ezMwj+DLqBBNVdUt4d2G4VTQ2bYi0g04DRhdiE5chP+JDAAeBVDVnaq6ISL544H/qOriAnUaAE1FpAFBY5DXtd8hBwDvquo2Va0C3gTOqqtImnV5MEGzRvj3zEL0VHWeqn5SV29ZNF8L6wZ4l+Ba+kL0NiXdLaMO20uG/cGDwE110cqilzdpNH8C3KOqO8LnrC5QDwAREeA84OkC9RSoGWFoSR23lzSavYC3wtuTgbProJduX53X9pJOr9DtxSVMaDC6AkuT7i+jwC/wuBCRHkBfghGHQrVKwyHK1cBkVS1U87cEO8tEgTo1KPCaiLwvIlGk1+0DrAEel+AwzmgRKYtAF4LrtHPeWdaGqi4Hfg0sAVYAG1X1tQIk5wADRKStiDQj+A+ve5bX5EpHVV0BwU4Q6BCRblxcBrxSqIiI3C0iS4ELgZ8VqHUGsFxVPyzUVxJXhYdxHqvLYasM7A98T0SmicibInJEBJoA3wNWqeqnBeqMAO4PP5NfAyMLNUaw3ZwR3j6XPLeZlH11wdtLlPt+lzChwYgsljRORKQcGAeMSPlvKi9UtVpV+xD8Z3ekiBxcgLfTgdWq+n6hvpI4WlUPB04BhovIgAL1GhAMhz6sqn2BrQTDlQUhQRDMGcDfC9RpTfCfTk+gC1AmIhflq6eq8wgODUwGJhEc+jPt951jR0RuI6h7bKFaqnqbqnYPta4qwFMz4DYKbFJSeBjYF+hD0KA+EIFmA6A10B/4KfBsOPpQKD+kwIY85CfAdeFnch3h6GSBXEawv3mf4LDEzroKRL2vjlrPJUxoMCKLJY0LEWlIsIKNVdXno9QODxNMBU4uQOZo4AwRWURwiGmgiIwp0NcX4d/VwAsEh7IKYRmwLGmk5jmChqNQTgE+UNVVBeqcACxU1TWqugt4HjiqEEFVfVRVD1fVAQRDwYX+x1jDKhHpDBD+zXnovD4RkYuB04ELVTXKfxqeog5D57WwL0Ej+WG4zXQDPhCRTvkKquqq8J+GBPAIhW8vEGwzz4eHVKcTjE7mfDJqbYSH/4YAf4vA38UE2wkEDX7BNavqfFUdpKrfJmiC/lOX16fZV+e9vcS573cBExqMyGJJ4yD8j+FRYJ6q/iYizfYSnlUvIk0Jvtzm56unqiNVtZuq9iB4/15X1bz/+xaRMhFpXnOb4IS9gq7IUdWVwFIR6RXOOh6YW4hmSFT/jS0B+otIs/AzP57gmGveiEiH8O9eBDv1KHxCsH1cHN6+GJgQkW5kiMjJwM3AGaq6LQK9/ZLunkFh28tHqtpBVXuE28wygpP5Vhbgr3PS3bMocHsJGQ8MDPX3Jzgxem2BmicA81V1WYE6EPwjeEx4eyARNNBJ20wJcDvwpzq8Nt2+Oq/tJY59v3PEfRZpLhPB8ekFBN3qbRHoPU0wTLmLYOdxeQFa3yU4ZDMbmBVOpxbo71BgZqg5hzqczZ2D9rEUeBUJwfkSH4bTx1F8JqFuH4KfB55NsPNsXaBeM6ASaBmRvzsIvrjmAH8lPHu/AL3/I2iiPgSOz1Njt3UZaAtMIdihTwHaFKh3Vnh7B7AKeDUCj58RnFtVs83U5aqP2vTGhZ/LbOBFgpPt8tZLeXwRdbuKpDZ/fwU+Cv39A+gcwXvYCBgT1v0BMLDQmoG/AFdGtB5+F3g/XL+nAd+OQPNagu+CBcA9hGnTOerVuq/Od3vJoFfQ9uLS5KPCPR6Px+PxRI4Jh0g8Ho/H4/HsYfgGw+PxeDweT+T4BsPj8Xg8Hk/k+AbD4/F4PB5P5PgGw+PxeDweT+T4BsPj8Xg8Hk/k+AbD4/F4PB5P5Px/c7p73nve2jwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_lr)\n",
    "plt.title('Heatmap of Confusion Matrix Logistic Regression ', fontsize = 15)\n",
    "sns.heatmap(cm, annot = True,cmap='Wistia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7693a472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        18\n",
      "      banana       1.00      1.00      1.00        18\n",
      "   blackgram       0.86      0.82      0.84        22\n",
      "    chickpea       1.00      1.00      1.00        23\n",
      "     coconut       1.00      1.00      1.00        15\n",
      "      coffee       1.00      1.00      1.00        17\n",
      "      cotton       0.89      1.00      0.94        16\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.84      1.00      0.91        21\n",
      " kidneybeans       1.00      1.00      1.00        20\n",
      "      lentil       0.94      0.94      0.94        17\n",
      "       maize       0.94      0.89      0.91        18\n",
      "       mango       1.00      1.00      1.00        21\n",
      "   mothbeans       0.88      0.92      0.90        25\n",
      "    mungbean       1.00      1.00      1.00        17\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        23\n",
      "      papaya       1.00      0.95      0.98        21\n",
      "  pigeonpeas       1.00      1.00      1.00        22\n",
      " pomegranate       1.00      1.00      1.00        23\n",
      "        rice       1.00      0.84      0.91        25\n",
      "  watermelon       1.00      1.00      1.00        17\n",
      "\n",
      "    accuracy                           0.97       440\n",
      "   macro avg       0.97      0.97      0.97       440\n",
      "weighted avg       0.97      0.97      0.97       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#lets print the Classification Report also\n",
    "from sklearn.metrics import classification_report\n",
    "lr=classification_report(y_test, y_pred_lr)\n",
    "print(lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98925023",
   "metadata": {},
   "source": [
    "## Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9317598a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Classifier accuracy= 0.9931818181818182\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes Classifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb_classifier = GaussianNB()\n",
    "nb_classifier.fit(x_train, y_train)\n",
    "y_pred_nb = nb_classifier.predict(x_test)\n",
    "nb_acc=accuracy_score(y_test, y_pred_nb)\n",
    "print('Naive Bayes Classifier accuracy=',nb_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9601d78a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Jz33HxF5EHg6WAfTMI3CvYE9VPXnQSP9Z5hLliuDOv4b0zhJx3mYK62mBftEfdDWiZht+EdsbjAlknH7yrQfichYTANnOmY7/C/MukzVK5ctt5Df5+opNIUaTRr1hNn4lqZ47jGSRkpjBq89iumO3ogZKDQS2DvhNTdgdsJ1mEu+didptD3mevP3g9f8MEo++XXBvMlsfaXDFr4xl7U+itmBl2O6Awey5dUN3YP/z8NcyrgG02X7O0CyWFdXYr4sqzGXv12b9PzRicvLQq8P5iC/CNOj8V2wLg9IeE1p4O/bYLmfA+dl8TnV1zow3XoM5g8L9NcEyz8tab1dhLnMbTmmR2UGcEm6ujGDU/+csJ1MA07M4nPNuA6zfM0WV5Fks30ErzsP+CJY15ppeSRsr5hGXy1wRNJrRmP2k5RXNNDwdl+GOTW2xVUkmJ6RTzFfQjMwDc4t1mVD20Qw/+eB3r9T+NgrWHfLA/2vMFebdEvj/SK2vHqkJqj370BF0mszHhsSXvv7YHtr08Bzghn38FmwfS0B3gIGB8/vGdTxbfD8fEzPUvtUdSRodwLux+zjGzE9Sa8CZ6Vav9lsX2Tej/4Hs5+swuyLU4DTG1jPjbqKJJvPlUYeu/wU7STBh+JxCDFhSt8Ap2pCmJXH47EPEZmK6dm8oNhePJ5CEtdTJB6PxxMpYkLKjsVcHZM2AM/j2RaJ6yBPj8fjiZoPMKfthqnqB8U24/Fkg4jsKCa2f5aYWzpcE8y/RUQWiImq/1iSIvsb1PKnSDwej8fj8YC5Hw0mg+Wj4CqkDzGDzwdhcoEavGqwIfwpEo/H4/F4PACoahVBdpGqrhGRWWSfSLwF0fdgjJRQF7Dg/JvClPN4PB6PJ3S68oewItKzovaW7L5rm/yO/8YkBtczXFUbvDFjcEHB25j8keswVwCtxlwldL1ueXuErZeVjaGwGTa+gslftqK8VS0TLp0HwKyFzfntSx3ZWCOUlsAtJy9m366NimX4galvL+Qvf/iYujrllLN34dwhyflLxdVzwaOv2T49Fzz6mu3Tc8FjHGsOnSybM0FjIuOdnoN76IwBhqrqahH5Gyb9WoO/92AyWlJSlEGeZ+23mofP3TJP565JHbii3zLGDankmqOWcdekDjlp19Yq9986ndsfPoJHXzyRNyZ8y9yvVmd+Y4H0XPDoa7ZPzwWPvmb79FzwGMeabUdEmmIaF6NU9XkAVV2kqrVqghwfwtynJi1FaWActPMG2rao3WKeAOs2GjtrNpbQsWyrG0lmxewZy+m6cxlddiyjabMSjh2wI+9N+i5nr2HrueDR12yfngsefc326bngMY41R4JkOWWSMbdDGAHMUtU/JcxPvInmmZiQvLRkbGCIyF4icqOIPCAi9wePe2a22Thu6r+EO1/fgaPu34U7Xt+B647N5Z5EsHTRBjp2avHD/x0qWrBk0YacfYWt54JHX7N9ei549DXbp+eCxzjWbDmHY+4SfGzSJal3ishMEZmBiYe/NpNQ2jEYInIjJjb4aUwGPJh7BjwlIk+ruclNQ+8bQjCI5O8Xw5BjM1f01IdtGdZ/CSf2XMtLn5Vx84QKHjs/420JtqKhMauSx1CbsPWi0LRdLwrNuOlFoWm7XhSacdOLQtN2vSg0o/AYOiH5UdV3U6i91FitTIM8L8Hcm2NT4szg5kWfAQ02MLYYRJLlVSRjZ7Th5hPNTTVP7rWWX0+oyPCOhtmhUwsWL9zcsly6aAMdOrZI847C6rng0ddsn54LHn3N9um54DGONUeCbQ0eMp8iqcPcFCyZzsFzodGxrIap88wH9v7cFnRvvynDOxpmr33asWDuWqq+Xcem6jreePFbDj22c+Y3FkjPBY++Zvv0XPDoa7ZPzwWPcaw5LmTqwRgKTBKRL9l8a9+dMLfvvTLXhV73fCemzmvJivWl9LtvF646ahm3DVzEH1/tSE2d0LxJHbcOXJSTdmmTEq76zf7c+PN3qK1VTv5xd3bZvW2uVkPXc8Gjr9k+PRc8+prt03PBYxxrjgQLezAyBm2JSAnmcpSumBLmAx+oam3aN9bjg7Y8Ho/HEzMKHrT1h+y+a0tv1oL5yhi0FVzz+n6uCwi7QdB15B9D1QPfaPF4PB6P21g36BRL7kWSb0Kaa8mgUWjarueCR9v1XPDoa7ZPzwWPcaw5dCxsYBT9du1hJKS5lAwahabtei54tF3PBY++Zvv0XPAYx5rjQtEbGGEkpLmUDBqFpu16Lni0Xc8Fj75m+/Rc8BjHmiMhpCTPMCl6AyOqhDRbk0Gj0LRdzwWPtuu54NHXbJ+eCx7jWHNcKHoDI6qEtPpk0Leu+YZhJyzm5hyDu+KYMudrtk8vCk3b9aLQjJteFJq260Wh6UySp+/B2JKoEtLGzmhD/73WAiYZdMaC7azxZ3vKnK/ZPj0XPPqa7dNzwWMca44E38DYmqgS0mxNBo1C03Y9FzzarueCR1+zfXoueIxjzXGh6JephpGQ5lIyaBSatuu54NF2PRc8+prt03PBYxxrjgTbTtmQRZJnvizg5lAX4IO2PB6Px2M7hU7yrLsruyTPkv+xKMnTNqJoDHS+L9xGS9VQ32DxeDweTwGxsAfDigaGdYlrZd2QEx+HVp1A69CZw+HjB5Aj7oQep0JtNayag068GDauKo5Hx/Rc8Gi7ngsefc326bngMY41x4GiD/K0MnGtrgZ9+3r08V7o04cg+10B7XuilRPRJ3qjo/aDlV8gBw0rnkeH9FzwaLueCx59zfbpueAxjjVHgr+KZGusTFxbvxCWTDePN62F5bOgrCtUToTgJrJa9T6UdSueR4f0XPBou54LHn3N9um54DGONUeCiw0MEdlLRI4TkbKk+SeFYcD6xLU2O8MOfWDhlC1my94/Q+e+bIVH2/Vc8Gi7ngsefc326bngMY41x4W0DQwRuRoYB1wFfCoipyc8nXJkpIgMEZFpIjJt5PDpaQ1YnbjWtBUyYAz61lCoXrN5/kE3QV0NzB6Vk6zVNUegF4Vm3PSi0LRdLwrNuOlFoWm7XhSaPskzNzIN8vwFcKCqrhWR7sBzItJdVe8njVVVHQ4Mh8yXqVqbuFbSBBk4Bp09CuaM3Ty/52Ckx0B0zHHF9+iIngsebddzwaOv2T49FzzGseZIsK3BQ+ZTJKWquhZAVecCRwMni8ifCKkcWxPX5PgRZuzF9Hs3z9z5RKTvjej406Am9+4xW2uOSs8Fj7brueDR12yfngse41hzXMjUg7FQRPZX1Y8Bgp6MgcAjwD5hGLAyca3L4UivweiSGch55hSP/usm5OgHoLQ5ctZE87qq99E3LiuOR4f0XPBou54LHn3N9um54DGONUeBdadsyJDkKSLdgBpVXdjAc4er6r8yLSDsJM8o8EFbHo/H4wmTQid56gPZJXnK1ZYkearq/DTPZWxcuELYDYLOd4bcYLnBN1g8Ho/H4xY+ybNQeq27IQMS0kE/GQ4fPoAccSvsdjpoHaxfjL58EaytKo7HCPVc8Gi7ngsefc326bngMY41h46Fp0iKHrRle+JaaHp1Neib16MjeqEjD0H6XAHlPdGpd6GP7Yf+ow86ZwJy2G+K5zEiPRc82q7ngkdfs316LniMY81xoegNDNsT10LTW7cQFgWZINVrYVmQDpqYr9G0VcMXXBfKY0R6Lni0Xc8Fj75m+/Rc8BjHmiPBwhyMojcwbE9ciyTBrc3OUNEHqkw6qBz5e+TSSqTXeei7je/BcKFm2z3arueCR1+zfXoueIxjzZHgGxhbY3viWugJbk1bIWeMQScN/aH3Qt/5NfrgTujno5ADriy6xzgm69muF4Wm7XpRaMZNLwpN2/Wi0HQiydNCit7AsD1xLVS9kiamcfH5KPhy7NbPz3oS9vhxcT1GoOeCR9v1XPDoa7ZPzwWPcaw5EnwPxtbYnrgWpp6cNMKMvZiWkA7abrfNj3c7DZbPLqrHKPRc8Gi7ngsefc326bngMY41x4WiX6Zqe+JaaHpdD0d6D0YXz0AuDNJB37kJ2ecSaL+nuUx19Tz0tUuL5zEiPRc82q7ngkdfs316LniMY82RYOEpm7RJnmHgQpJn2PigLY/H44k3BU/yfDDLJM9LLUny9ORG2A2CruPDbbAALDjNN1o8Ho/HEx1WNDBsT1yzMWVu2OgKJn/eivKyWib8ch4Af36tnNFT2tK+VQ0A1528jKN6riuKv0Joxk3PBY++Zvv0XPAYx5pDJ6R+CRHZEXgc6ATUAcNV9X4RaQ88A3QH5gKDVHVFOq2iD/K0PXHN1pS5s/qu5uGfL9hq/kVHrmDcdZWMu64y58aFrTXHWc8Fj75m+/Rc8BjHmi2nBrheVXsChwBXiEgv4FfAJFXdHZgU/J+WojcwbE9cszVl7qAeG2jbsjYvH1H6i1ozbnouePQ126fngsc41hwJIV2mqqpVqvpR8HgNMAvoCpwO/CN42T+AMzJpNbqBISKPN/Y96bA9cc2FlLlERr23PafeszPDRlewan1u7UcXao6bngsefc326bngMY41FxMRGSIi0xKmIWle2x3oA0wBKlS1CkwjBOiYaVlpx2CIyPjkWcAxIrJ9sJDTUhUADAG4/e8nc/6QPimXYXvimgspc/X89NCVXH78MgS4/9Vybp+wA/87aJEV/mz/XGzXi0LTdr0oNOOmF4Wm7XpRaDqR5JmlH1UdDgzPKCdSBowBhqrqasmh4EyDPLsBnwMPA4opoS9wT7o3JRaQ6TJV2xPXXEiZq6dD682nTM7+0SoufaSrNf5s/1xs13PBo6/ZPj0XPMax5igIs8EjIk0xjYtRqvp8MHuRiHRW1SoR6QwszqSTqQ+9L/AhcDOwSlUnAxtU9S1VfSt3+5uxPXHNhZS5ehavLv3h8euflrF7p43W+LP9c7FdzwWPvmb79FzwGMeabUZMV8UIYJaq/inhqfHAhcHjC4FxmbTS9mCoah1wr4g8G/xdlOk9jcX2xDVbU+auG9WJqXNasmJdKf1+vwtX9V/G1Dktmf1dcwC6tt/ErT9u/OmRsPxFrRk3PRc8+prt03PBYxxrjoTwejAOBy4AZorIx8G8m4DbgdEicglQCZyd0VJjkjxFZABwuKpmndIUxyTPsPFBWx6Px+MWhU7y5JHskjz5maVJnqr6IvBiRF48KYiiMdD1mXAbLQvO8Q0Wj8fjKRq2DTrFJ3kWRc9Wj8PGVjD5i1aUt6plwpUmHXTo6M58s7QpAGu+L6X1drWMu7yyaB7jrOeCR1+zfXoueIxjzXGg6EFbtieuxSll7qw+q3n4gi3TQe8bVMW4yysZd3kl/Xut4YSea4vqMa56Lnj0Ndun54LHONYcCSEFbYVJ0RsYtieuxSll7qDuG2jbouF0UFV4+dPWDNx3TVE9xlXPBY++Zvv0XPAYx5rjQtEbGLYnrvmUOcO0eS0oL6ule/mmnN5ve82267ng0ddsn54LHuNYcyT4HoytsT1xzafMGSbMbM3AfXLrvQD7a7ZdLwpN2/Wi0IybXhSatutFoelEkqeFFL2BYXvimk+Zg5pamPh5Gaf0zr2BYXvNtuu54NHXbJ+eCx7jWHMk+B6MrbE9cc2nzMF7X7ekR4dqOrWtscZj3PRc8Ohrtk/PBY9xrDkuFP0yVdsT1+KUMnfds52Y+k1LVqwvpd/du3DVMcs4+8DVvDSzNQNyHNwZtse46rng0ddsn54LHuNYcyRYeMqmUUmeueCTPO3EB215PB5PdBQ8yfOJLJM8L7A0yTMqbA9E2RZDYDI1CBqr1/X5rRssw56rYPLsVpSX1TJh6Lwf5j/x3vaM/Pf2NClRjtprHTecvHRrf2dlbrAUex0WWs8Fj75m+/Rc8BjHmkPHwh6Moo/BsD0QJY4hMKEFdx24mocv3jK46/05LZj0eSteuGYeL147j0uOXFFUj67oueDR12yfngse41hzXCh6A8P2QJQ4hsCEFty1ywbattwyuOupKdsz5OgVNGtievPKyxoO9iqUR1f0XPDoa7ZPzwWPcaw5EvxVJFtjeyBKHENgogyVmbu0KdO+acHZf92R84d3Y8a3za3waLueCx59zfbpueAxjjVHgmsNDBH5kYi0CR63EJHficgLInKHiIQyhNb2QJQ4hsBEGSpTWyes3lDC6Mu/5YaTlzL0qS4NLi8Tttfst5v89aLQjJteFJq260Wh6YO2ciNTD8YjwPrg8f1AW+COYN6jqd4kIkNEZJqITBs5fHraBdgeiBLHEJgoQ2Uq2tRwQu+1iMC+O35PiSgr1pUW3aPtei549DXbp+eCxzjWHAmu9WAAJapan67UV1WHquq7qvo7oEeqN6nqcFXtq6p9zx/SJ+0CbA9EiWMITJShMsfvvZb357QE4JslTdlUK7Rr1fhxGLbX7LcbX7MNei54jGPNcSHTZaqfisjFqvoo8ImI9FXVaSKyB5DbXa+SsD0QJY4hMKEFdz0VBHetK6Xf/+7CVccv48cHruKmMZ0YeN/ONC1Vbj97YU5djbbWHJWeCx59zfbpueAxjjVHgoWnbNIGbQXjLO4HjgSWAgcA3wbT1ar6SaYF+KCteNBQDkY+ZJOD4fF4PLZS8KCtp7MM2vqJJUFbqroKuEhEWmNOiTQB5qvqokKY83g8Ho/HkwUW9mBkleSpqmuAjL0VuWJ74locU+YanQwacvJm15ENJIOOr2Dyl60ob1XLhEtNMuishc357Usd2VgjlJbALScvZt+u3zfs8fxw00sz4bcbX7MNei54jGPNcaDoORi2J67FMWXO1prP2m81D5+7ZTLoXZM6cEW/ZYwbUsk1Ry3jrkkdiuYvSj0XPPqa7dNzwWMca44EB68iiRzbE9fimDJna80H7byBti22vOJEgHUbzWa8ZmMJHctyu6V8XNahS3oueLRdzwWPcaw5LhS9gWF74locU+ZcqLmem/ov4c7Xd+Co+3fhjtd34Lpjt75xWjH8ubAObddzwaPtei54jGPNkeB7MLbG9sS1OKbMuVBzPU992JZh/Zfw1jXfMOyExdw8oSInnTiuQ9v1otCMm14UmrbrRaHpRJKnb2Bsje2Ja3FMmXOh5nrGzmhD/73WAnByr7XMWLCdFf5cWIe267ng0XY9FzzGsea4UPQGhu2Ja3FMmXOh5no6ltUwdZ7Z0d+f24Lu7XPLf4vjOrRdzwWPtuu54DGONceFrC5TjRLbE9fimDJna83XPd+JqfNasmJ9Kf3u24WrjlrGbQMX8cdXO1JTJzRvUsetA3OLaInLOnRJzwWPtuu54DGONUeCbadsyJDkGQY+ydOTCw3lYORLphwMj8fjCYuCJ3mOyTLJ88eWJHl6PMUiisZAl7+G22j57grfYPF4PJZgYQ+GFQ0M2xPX4pgyF4eaq9Y04cbXOrF0fSklAoN6r2Lw/it55csy/jKlnDnLmzH6nEr2qdhYFH+F0LRdzwWPtuu54DGONYeOhQ2Mog/ytD1xLY4pc3GpubREufHIJbx0wTyeHlTJqBnb89WyZuxeXs0DA76jb9fcr3OPyzqMUs8Fj7brueAxjjXHhaI3MGxPXItjylxcau7Yqpa9O5reibJmyq7tqlm0rgm7tq+mR7vcrkYJ01/UmrbrueDRdj0XPMax5khwLQdDRJqJyGAROT74/1wR+YuIXCEiTcMwYHviWhxT5uJY8/zVTZi1pDn7VTR8o7TGEsd16Gu2T88Fj3GsOS5k6sF4FBgAXCMiTwBnA1OAg4CHU71JRIaIyDQRmTZy+PS0C7A9cS2OKXNxq3ldtXD1i10Y1m8JZc3rcjeVQNzWYRR6UWjGTS8KTdv1otCMU5KniDwiIotF5NOEebeIyAIR+TiYTsnGUqZBnvuo6r4i0gRYAHRR1VoRGUma27er6nBgOGS+TNX2xLU4pszFqeZNtXD1S104dc/V9N9tbc5+ovIXpabtei54tF3PBY9xrNlyHgP+AjyeNP9eVb27MUKZejBKRKQZ0BpoCdQnizQHQjlFYnviWhxT5uJSsyr8elIndm1fzcUHrMzZS1T+ota0Xc8Fj7brueAxjjVHQkg9GKr6NrA8DEuZejBGALOBUuBm4FkR+Ro4BHg6DAO2J67FMWUuLjV/VLUd42a3YY/yjZzx5E4AXHvYMqprhd9P3oHlG0q5dHxX9tphIyPOWFBwf1Fr2q7ngkfb9VzwGMeai4mIDAGGJMwaHpx1yMSVIjIYmAZcr6orMi4rU5KniHQBUNXvRGR74HigUlWnZmHIJ3l6rMEHbXk8nkJR8CTPF7JM8jw1c5KniHQHJqhq7+D/CmApoMBtQGdV/VkmnYxBW6r6XcLjlcBzmd7j8dhI2A2CLsNDbrAM8Q0Wj8djH6r6w02eROQhYEI27/NJnkXQc8GjrzmHZNDVTbjxlU4sXRckg+67isEHrGTlhhKum9CZBaub0rXNJu49tYq22+V2tYptNUet54JH2/Vc8BjHmkMnwv4SEemsqlXBv2cCn6Z7fT1FD9qyPXEtjilzvuY8kkGPWsJLF8/j6XMrGfWxSQZ9aGp7DtlpPa9eMpdDdlrPQ1PbF82jS3oueLRdzwWPcaw5EsK7TPUp4N/AniIyX0QuAe4UkZkiMgM4Brg2G0tFb2DYnrgWx5Q5X3OOyaBltexdkZAM2r6aRWuaMGlOGWfsbQ5GZ+y9mte/KiuaR5f0XPBou54LHuNYs82o6k9VtbOqNlXVbqo6QlUvUNV9VHVfVT0toTcjLUVvYNieuBbHlDlfcwjJoKuaMGtxc/br/D3L1pfSsawWMI2Q5etLrfBou54LHm3Xc8FjHGuOBNeiwguB7YlrcUyZ8zXnp7euWrh6fBeGHRNeMijYXXMUelFoxk0vCk3b9aLQdCLJ00KK3sCwPXEtjilzvuY8k0HHd+HUnqvpv7tJBi1vWcvitabXYvHaUtq3rC2qR1f0XPBou54LHuNYcyT4HoytsT1xLY4pc77mPJJBX+vEruXVXNx35Q/zj911Lf/8rA0A//ysDcftmlskuY01R6nngkfb9VzwGMea40LRL1O1PXEtjilzvuYck0EXbMe4z9uwR4eNnPF4kAx6xDJ+cfByrp3QhTGftqVzmxruG5jb4DAba45SzwWPtuu54DGONUeChadsMiZ55otP8vRsq/igLY/Hk4qCJ3m+kmWS50mZkzzDoug9GB6Pq4TdIOj6TLgNlgXn+AaLxxMbLOzBsKKBYXviWhxT5nzNdugNG1vB5C9aUd6qlglXzgNg6OjOfLPU3Mx4zfeltN6ulnGXVxbNY5R6Lni0Xc8Fj3GsOQ4UfZCn7YlrcUyZ8zXbo3dWn9U8fMGWd3K9b1AV4y6vZNzllfTvtYYTeuY2aNTWml3yaLueCx7jWHMkuHYViYi0FZHbRWS2iCwLplnBvO3DMGB74locU+Z8zfboHdR9A21bNHxZqyq8/GlrBu67pqgeo9JzwaPtei54jGPNkeBaAwMYDawAjlbVclUtx+SQrwCeDcOA7YlrcUyZ8zXbp9cQ0+a1oLyslu7lm3J6vws12+7Rdj0XPMax5riQqYHRXVXvUNWF9TNUdaGq3gHslOpNIjJERKaJyLSRw6enXYDtiWtxTJnzNdun1xATZrZm4D659V6AGzXb7tF2vSg0bdeLQtMneeZGpgbGPBG5QUQq6meISIWI3Ah8m+pNqjpcVfuqat/zh/RJuwDbE9fimDLna7ZPL5maWpj4eRmn9M69geFCzbZ7tF3PBY9xrDkSHDxFcg5QDrwlIstFZDkwGWgPnB2GAdsT1+KYMudrtk8vmfe+bkmPDtV0altjjUe/3din54LHONYcF9JepqqqK4Abg2kLRORi4NF8DdieuBbHlDlfsz161z3bianftGTF+lL63b0LVx2zjLMPXM1LM1szIMfBnWF7jErPBY+267ngMY41R4KFp2xyTvIUkUpVTTkOox6f5OnxZIcP2vJ4th0KnuT5RpZJnsdakuQpIjNSPQVUpHjO4/HkQNgNgq4jw22wACw43zdaPB5PdmRK8qwATsRclpqIAO+FZcL2xLU4psz5mu3TC0Nz2PgKJn8ZJINeapJBZy1szm9f6sjGGqG0BG45eTH7dv2+KP4KoRk3PRc8xrHm0LHwspZMgzwnAGWqOi9pmosZ7Jk3tieuxTFlztdsn15Ymmftt5qHz90yGfSuSR24ot8yxg2p5JqjlnHXpA5F8xe1Ztz0XPAYx5ojwbWrSFT1ElV9N8Vz54ZhwPbEtTimzPma7dMLS/OgnbdOBhVg3UZzKFizsYSOZbldmWJrzXHWc8FjHGuOBJHspgJS9HuR2J64FseUOV+zfXpRaQLc1H8Jd76+A0fdvwt3vL4D1x271Bp/tn8utuu54DGONUeCaz0YhcD2xLU4psz5mu3Ti0oT4KkP2zKs/xLeuuYbhp2wmJsn5DZ+24Wa46YXhabtelFoupHkaV8Lo+gNDNsT1+KYMudrtk8vKk2AsTPa0H8vc0fWk3utZcaC7azxZ/vnYrueCx7jWHMk2Ne+KH4Dw/bEtTimzPma7dOLShOgY1kNU+eZg+X7c1vQvX1uN09zoea46bngMY41R4KFDYxMl6lGju2Ja3FMmfM126cXluZ1z3di6rwgGfS+XbjqqGXcNnARf3y1IzV1QvMmddw6cFHR/EWtGTc9FzzGseZosO6cTe5Jntnikzw9nuLgg7Y8nuJR8CTP95pl9117WLUdSZ4ej8ddomgMdPlruI2W767wDRaPZ1vFigaG7YlrcUyZ8zXbp2ejx6o1TbjxtU4sXV9KicCg3qsYvP9KXvmyjL9MKWfO8maMPqeSfSo2Fs1j3PVc8BjHmkPHvstaij/I0/bEtTimzPma7dOz1WNpiXLjkUt46YJ5PD2oklEztuerZc3YvbyaBwZ8R9+u+WUF2FizS3oueIxjzZFg4SDPtA0MEWkjIv8rIk+IyLlJz/1fGAZsT1yLY8qcr9k+PVs9dmxVy94dTe9EWTNl13bVLFrXhF3bV9OjXW5Xo4TtMc56LniMY81xIVMPxqOYNs8Y4CciMkZEmgfPHRKGAdsT1+KYMudrtk/PBY/zVzdh1pLm7FeR243SGsL2mm3Xc8FjHGuOBAejwndV1V+p6j9V9TTgI+ANESlP9yYRGSIi00Rk2sjh09MuwPbEtTimzPma7dOLQjNMvXXVwtUvdmFYvyWUNa/L3VQSNtfsgl4UmrbrRaHpRJKnhQ2MTIM8m4tIiarWAajqH0RkPvA2UJbqTao6HBgOmS9TtT1xLY4pc75m+/Rs9ripFq5+qQun7rma/rutzdlPQ9hasyt6LniMY82RYFuDh8w9GC8AxybOUNV/ANcD1WEYsD1xLY4pc75m+/Rs9agKv57UiV3bV3PxAStz9hKlxzjrueAxjjVHgoWDPNP2YKjqDSnmvyIioVwQb3viWhxT5nzN9unZ6vGjqu0YN7sNe5Rv5IwndwLg2sOWUV0r/H7yDizfUMql47uy1w4bGXHGgqJ4jLOeCx7jWHMkWHfOJo8kTxGpVNWdMr3OJ3l6PNsOPmjL48mOgid5ftgyu+/aA9en9SUijwADgcWq2juY1x54BugOzAUGqeqKTItK28AQkRmpngL2UNXmKZ7/Ad/A8Hg8qeh0S7gNloW3+AaLxw4K3sCY3iq779o+6zI1MPoBa4HHExoYdwLLVfV2EfkV0E5Vb8y0qEyDPCuAE4HklooA72USzxbbE9fimDLna7ZPzwWPoei16UbJmY9DWSfQOvTD4eiUB5Cjf4sc8AtYvwSAukk3wZcvF8ejQ3oueIxjzbaiqm+LSPek2acDRweP/wFMBjI2MDIN8pwAlKnqvKRpbrCAvLE9cS2OKXO+Zvv0XPAYml5dDXWvXU/dX3tR9/AhyMFXwA49AdD376XuwT7UPdgnp8aFtTVHpOeCxzjWHAlZXqaaGCMRTEOyUK9Q1SqA4G/HbCylbWCo6iWq+m6K585taH5jsT1xLY4pc75m+/Rc8Bia3tqFUBXk51SvhSWzoHXXnH1F4tERPRc8xrHmSMjyKhJVHa6qfROm4VFZKvq9SGxPXItjypyv2T49FzxGkna4/c7QuQ8smAKAHHwlJZd9gpw+ArbbvugebddzwWMca46GSK9TXSQinQGCv4uzeVPRGxi2J67FMWXO12yfXhSatuvRrBUlg8ZQ98pQ2LgG/eBv1N2/K3UP7g9rqpAT7ym6R9v1otC0XS8KTTeSPLOccmM8cGHw+EJgXDZvKnoDw/bEtTimzPma7dNzwWOoeiVNKBk0Bp05CmaNNfPWLQatA1X0o4eQrgcX16MDei54jGPNkRBSVLiIPAX8G9hTROaLyCXA7cAJIvIlcELwf0aK3sCwPXEtjilzvmb79FzwGKaenD4CXToL/fe9m2eWddr8/F5noos/LapHF/Rc8BjHmiMhpB4MVf2pqnZW1aaq2k1VR6jqMlU9TlV3D/4uz8ZSpstUI8f2xLU4psz5mu3Tc8FjaHo7HU7JfoPRRTOQS81gz7pJNyG9f4p02h9QWDmXuhf+u3geHdFzwWMca44E687Z5JHkmS0+aMvj8aTCB215tlUKHrT1Wdvsvmv3XlUwX0XvwfB4PPEl7AZB12fCbbAALDjHN1o8DmBfB4YdDQzbE9fimDLna7ZPzwWPNtY8bGwFk79oRXmrWiZcOQ+AoaM7883SpgCs+b6U1tvVMu7yyqL4i1rPBY9xrDl0LDxF0uhBniKSVYJXttieuBbHlDlfs316Lni0teaz+qzm4Qu2vJPrfYOqGHd5JeMur6R/rzWc0HNt0fxFqeeCxzjWHAkhXUUSJmkbGCLSPmkqB6aKSLvg7mp5Y3viWhxT5nzN9um54NHWmg/qvoG2LWobfE4VXv60NQP3XVM0f1HqueAxjjVHQrQ5GDmRqQdjKfBhwjQN6Ap8FDzOG9sT1+KYMudrtk/PBY8u1JzMtHktKC+rpXv5ppzeH8d1aLueKx7Dx74WRqYGxg3Af4DTVHUXVd0FmB887pHqTYk3Uxk5fHraBdieuBbHlDlfs316UWjarheVZiITZrZm4D659V5APNeh7XpRaPokz9xIO8hTVe8WkaeBe0XkW+C3QMZLYYKbpwyHzJep2p64FseUOV+zfXoueHSh5kRqamHi52U8f2lugzshnuvQdj1XPIaObQ0eshjkqarzVfVs4E1gItAyTAO2J67FMWXO12yfngseXag5kfe+bkmPDtV0altjjT8X1qHteq54DB0LB3lmfZmqqr4gIq8DuwKIyMWq+mi+BmxPXItjypyv2T49FzzaWvN1z3Zi6jctWbG+lH5378JVxyzj7ANX89LM1gzIcXBnmP6i1HPBYxxrjgTrztnkkeQpIpWqulOm1/kkT4/HUyh80JbHFgqe5DmnQ3bftbsutSPJU0RmpHoKqAjLhO2BKHEMgfE126fngsdi15xNY6CxHjvfmdRoad0NGfA4tOoEWod+Mhw+fAA54lbY7XRzx9f1i9GXL4K1VVvpVd2Q3mOx1+G2oOeKx1CxrwMj4xiMCmAwcGoD07IwDNgeiBLHEBhfs316LniMTc11Neib16MjeqEjD0H6XAHlPdGpd6GP7Yf+ow86ZwJy2G+K4y9iTdv1XPEYOhaOwcjUwJgAlKnqvKRpLjA5DAO2B6LEMQTG12yfngseY1PzuoWwKLj8vnotLJsFZV2hOmE8R9NWDV/bWAh/EWvarueKxziQtoGhqpeo6rspnjs3DAO2B6LEMQTG12yfngse41gzbXaGij5QNQUAOfL3yKWVSK/z0Hcb34MRx3UYx5ojoUSymwppqaBLawDbA1HiGALja7ZPLwpN2/Wi0AxVr2kr5Iwx6KShP/Re6Du/Rh/cCf18FHLAlcX1F5Gm7XpRaDoRtGUhRW9g2B6IEscQGF+zfXoueIxVzSVNTOPi81Hw5ditn5/1JOzx4+L5i1DTdj1XPIaOg2MwIsf2QJQ4hsD4mu3Tc8FjnGqWk0aYsRfT7t08s91umx/vdhosn100f1Fq2q7nisfQcS0qvBDYHogSxxAYX7N9ei54jE3NXQ9Heg9GF89ALjSDPfWdm5B9LoH2e5rLVFfPQ1+7tDj+Ita0Xc8Vj+Fj3zmbnIO2ssUHbXk8HpfZKgcjTzLlYHi2DQoetLWgU3bftV0X2hG05fF4PHEn7AaBb7B4osG+HgwrGhi2J67FMWXO12yfngsefc056OWZDFoQj47pueIxVOxrXxR/kKftiWtxTJnzNdun54JHX7N9yaCheXRIzxWPobMtXEUiIuVhGrA9cS2OKXO+Zvv0XPDoa7YvGTQ0jw7pueIxDqRtYIjI7SLSIXjcV0S+BqaIyDwROSoMA7YnrsUxZc7XbJ+eCx59zfYlg0bh0XY9VzyGjoWXqWbqwRigqkuDx3cB56jqbsAJwD2p3iQiQ0RkmohMGzl8etoF2J64FseUOV+zfXpRaNquF4Wm1XoRJIOG7tEBvSg0nUjylJLspgKSaWlNRaR+IGgLVf0AQFW/AJqnepOqDlfVvqra9/whfdIuwPbEtTimzPma7dNzwaOv2b5k0FA9OqLnisewUSnJaiokmZb2V+AlETkWeEVE7hORfiLyO+DjMAzYnrgWx5Q5X7N9ei549DXblwwapkdX9FzxGD72nSNJe5mqqv5ZRGYClwF7BK/fA/gncFsYBmxPXItjypyv2T49Fzz6mu1LBg3No0N6rngMG83ymo1CNjFyTvIUkYtV9dFMr/NJnh6Px7MZH7QVDwqd5Fm3pEdW37UlO3xdMF/5NDAqVXWnTK/zDQyPx+OJji5/DbfB8t0VvsESBoVvYOyWZQPjKzuiwkVkRqqngIqwTNieuBbHlDlfs316Lnj0NduhV7WmCTe+1oml60spERjUexWD91/JK1+W8Zcp5cxZ3ozR51SyT8XGonmMUs8Vj2GiUlpsC1uR6aRNBTAYOLWBaVkYBmxPXItjypyv2T49Fzz6mu3RKy1RbjxyCS9dMI+nB1Uyasb2fLWsGbuXV/PAgO/o2zX3DAdba3bNY9goktVUSDI1MCYAZao6L2maC0wOw4DtiWtxTJnzNdun54JHX7M9eh1b1bJ3R9M7UdZM2bVdNYvWNWHX9tX0aLcpZ39heoxKzxWPoRNiDoaIzBWRmSLysYhMy9VS2qWp6iWq+m6K587NdaGJ2J64FseUOV+zfXouePQ126cHMH91E2Ytac5+Fd/npVOPCzW74DFsIujBOEZV91fVvrl6KvrdVG1PXItjypyv2T69KDRt14tCM25666qFq1/swrB+SyhrXpe7UAK21xyFphNJnsW/d+lWFN2R7YlrcUyZ8zXbp+eCR1+zXXqbauHql7pw6p6r6b/b2pw9JWNzzS55DBuV0qymxFt5BNOQhuSA10TkwxTPZ0XRGxi2J67FMWXO12yfngsefc326KnCryd1Ytf21Vx8wMqc/UTpMSo9VzyGT3ZJnom38gim4Q2IHa6qBwAnA1eISL9cHBX9FIntiWtxTJnzNdun54JHX7M9eh9Vbce42W3Yo3wjZzxp4oquPWwZ1bXC7yfvwPINpVw6vit77bCREWcsKIrHqPRc8Rg2Yd5nRFW/C/4uFpGxwMHA243VyTloK1t80JbH4/FEhw/aspNCB21tXNE3q+/a5u2mpfUlIq2AElVdEzyeCNyqqq801lPRezA8Ho/HkzthNwg63xdugwWgaqhvtERNtvciyYIKYKyYUaxNgCdzaVzUv7no2J64FseUOV+zfXouePQ126cXimZZN+TEx6FVJ9A6dOZw+PgB5Ig7ocepUFsNq+agEy+GjasK768AmrYneYZ1WYuqfg3sF4ZW0Qd52p64FseUOV+zfXouePQ126cXmmZdDfr29ejjvdCnD0H2uwLa90QrJ6JP9EZH7Qcrv0AOGlYcfxFrupHkWZrVVEjSNjBEpK+IvCkiI0VkRxGZKCKrROQDEekThgHbE9fimDLna7ZPzwWPvmb79ELTXL8QlphbybNpLSyfBWVdoXIiaC0AWvU+lHUrjr+INeOW5BkWmZb2f8CdwIvAe8DfVbUt8KvgubyxPXEtjilzvmb79Fzw6Gu2Ty8SzTY7ww59YOGULWbL3j9D575cfH8RaMY0yTNvMjUwmqrqy6r6FKCq+hzmwSRgu1RvSgzyGDl8etoF2J64FseUOV+zfXpRaNquF4Vm3PRC12zaChkwBn1rKFSv2Tz/oJugrgZmjyquv4g0nUjytLAHI9Mgz+9FpD/QFlAROUNV/ykiRwG1qd4UBHcMh8yXqdqeuBbHlDlfs316Lnj0NdunF6pmSRNk4Bh09iiYM3bz/J6DkR4D0THHFddfhJouJHlS4N6JbMjUnLkUuB74GXAicIyIrMScHrk6DAO2J67FMWXO12yfngsefc326YWpKcePMGMvpt+7eebOJyJ9b0THnwY1uZ0ysLnmKD2GjVKS1VRI0vZgqOonmIZFPdcEEyJyMWZcRl7YnrgWx5Q5X7N9ei549DXbpxeaZpfDkV6D0SUzkPPMaW/9103I0Q9AaXPkrInmdVXvo29cVnh/EWu6keRZ2CtEsiHnJE8RqVTVnTK9zid5ejwejzv4oK1wKHSS59o1/bP6ri1r/VrBfKXtwRCRGamewqR9eTwej2cbIorGQOc7w220VN0QvwZLJsK8F0lYZBrkWYE5RbIiab4QwumRemxPXItjypyv2T49Fzz6mu3Ts9Jj627IgIRk0E+Gw4cPIEfcCrudDloH6xejL18Ea6uK4zFivfBxb5DnBKBMVeclTXOByWEYsD1xLY4pc75m+/Rc8Ohrtk/PWo91Neib16MjeqEjD0H6XAHlPdGpd6GP7Yf+ow86ZwJy2G+K5zFCvWgoyXIqrKOUqOolqvpuiufODcOA7YlrcUyZ8zXbp+eCR1+zfXrWely3EBYFGUnVa2FZkAyamK3RtFXDARSF8hihXhSolGY1FZKin7SxPXEtjilzvmb79Fzw6Gu2T88Jj212hoo+UGWSQeXI3yOXViK9zkPfza0Hw/qaI0BFspoKSdEbGLYnrsUxZc7XbJ9eFJq260WhGTe9KDRDTwY9Yww6aegPvRf6zq/RB3dCPx+FHHBl8T1GoBcNjp0iKQS2J67FMWXO12yfngsefc326VntsaSJaVx8Pgq+HLv187OehD1+XFyPEelFgYv3Iokc2xPX4pgy52u2T88Fj75m+/Rs9ignjTBjL6YlJIO2223z491Og+Wzi+oxKr1IcPBeJJFje+JaHFPmfM326bng0ddsn561HrsejvQejC6egVwYJIO+cxOyzyXQfk9zmerqeehrlxbPY4R60WDdOZvckzyzxSd5ejweT7yJY9BWoZM8V3z/k6y+a9tt97QdSZ4ez7ZE63UzQ9Vb02qfUPU8nm2VsBsEYceZbwtR5oW+kVk2WNHAsD1xLRbJehHr2e5x48Y6hvz8WzZVKzW1ynHHtea/L+tgjb+oNG3Xc8Gj7XoueAxFr6wbcmJCOujM4fDxA8gRd0KPU6G2GlbNQSdeDBtXFcdjpNh3iiRtk0dE2orI7SIyW0SWBdOsYN72YRiwPXEtNsl6Eeq54LFZM+Fvf9+RJ5/pzpNPdeff/17HzBm5X+cex3Xoa7ZPzwWPoenV1aBvX48+3gt9+hBkvyugfU+0ciL6RG901H6w8gvkoGHF8xghKiVZTYUk09JGY+5DcrSqlqtqOXBMMO/ZMAzYnrgWm2S9CPVc8CgitGxpdoeaGqWmRvO6zj2O69DXbJ+eCx5D01u/EJYE6aCb1sLyIB20ciJoLQBa9T6UdSuex0iRLKfCkamB0V1V71DVhfUzVHWhqt4BZLxVezbYnrgWx2S9ONYM5lfKuT+ZS//jv+JHP2pF731yv849juvQ12yfngseI0nJbLMz7NAHFk7ZYrbs/TN07st2eAwbCy9TzbS0eSJyg4j8cGt2EakQkRuBb1O9SUSGiMg0EZk2cvj0tAuwPXEtdsl6EehFoRmFx9JS4cmnu/PiK7vy2Wcb+OqrjTlrxXEd+prt04tC03Y9mrZCBoxB3xq65b1NDroJ6mpg9qhGS7qQ5KmUZjUVkkwNjHOAcuAtEVkhIssxd1FtDwxK9SZVHa6qfVW17/lD+qRdgO2Ja7FK1otIzxWP9bRuXcqBB7bk3++ty1kjjuvQ12yfngseQ9UraYIMHIPOHgVzEtJBew5GegxEXzmv+B4jwrl7kajqCuBR4EpgR1Vtr6o9VfVG4OAwDNieuBanZL2o9FzwuGJFDWvWmPO0339fx9Qp6+nevZk1/qLQtF3PBY+267ngMUw9OX6EGXsxPSEddOcTkb43ouNPg5rcTms4keRp4b1I0l6mKiJXA1cAs4CHReQaVR0XPP1H4JV8DdieuBabZL0I9VzwuHRJDbf8diF1tUqdwvEntObIfmXW+ItC03Y9FzzarueCx9D0uhyO9BqMLpmBnBekg/7rJuToB6C0OXLWRPO6qvfRNy4rjsdIseycDRmSPEVkJnCoqq4Vke7Ac8ATqnq/iExX1fTnP/BJnh578EFbHs+2gQtBW4VO8lxcc1lW37Udm/zNmiTPUlVdC6Cqc0XkaOA5EdkZG5tLHk8afIPA49k2CLtB0PWZcBssAJzzh/A101DoAZzZkKmBsVBE9lfVjwGCnoyBwCNAaEdrK1PhItRzwaOv2T49Fzz6mu3Tc8GjjTUPG1vB5C9aUd6qlglXzgNg6OjOfLO0KQBrvi+l9Xa1jLu8Mm+voWDbZS1kHvExGFiYOENVa1R1MNAvDAPWpsJFpOeCR1+zfXouePQ126fngkdbaz6rz2oevmDBFvPuG1TFuMsrGXd5Jf17reGEnmvz8hkmSklWUyHJdBXJ/MSQraTn/hWGAWtT4SLSc8Gjr9k+PRc8+prt03PBo601H9R9A21b1Db4nCq8/GlrBu67psHni4N7SZ6RY3sqnE/W8zXboOeCR1+zfXoueHSh5mSmzWtBeVkt3cs3haaZNxYmeRb9bqq2p8L5ZL389aLQjJteFJq260WhGTe9KDRt14tKM5EJM1szcB+bei9ALbzuoug9GFanwkWg54JHX7N9ei549DXbp+eCRxdqTqSmFiZ+XsYpvS1rYEhpVlMhKXoDw+ZUuCj0XPDoa7ZPzwWPvmb79Fzw6ELNibz3dUt6dKimU9uaUPTCI7wkTxE5SUT+IyJficivcnVU9FMk1qbCRaTngkdfs316Lnj0Ndun54JHW2u+7tlOTP2mJSvWl9Lv7l246phlnH3gal6a2ZoBVg3uNIR1nxERKQX+CpwAzAc+EJHxqvp5o7XSJXmGgU/y9Hg8Ho/NRBO0pQUdFJHtd22mhFERORS4RVVPDP4fBqCq/9toU6pqxQQMsV3Tdj0XPNqu54JHX7N9ei549DXbp1eMCRgCTEuYhiQ9/1/Awwn/XwD8JZdlFX0MRgJDHNC0XS8KzbjpRaFpu14UmnHTi0LTdr0oNOOmV3BUdbiq9k2Yhie9pKEejpzORNjUwPB4PB6Px1Nc5gM7JvzfDcgp+cw3MDwej8fj8dTzAbC7iOwiIs2AnwDjcxEq+lUkCSR309ioabteFJpx04tC03a9KDTjpheFpu16UWjGTc86VLVGRK4EXgVKgUdU9bNctCK/isTj8Xg8Hk/88KdIPB6Px+PxhI5vYHg8Ho/H4wkdKxoYYcWSJug9IiKLReTTELR2FJE3RWSWiHwmIteEoLmdiEwVkU8Czd/lqxnolorIdBGZEILWXBGZKSIfi8i0kPxtLyLPicjsYH0emofWnoG3+mm1iAzN09+1wefxqYg8JSLb5al3TaD1Wa7eGtqWRaS9iEwUkS+Dv+3y1Ds78FgnIn1D8nhX8DnPEJGxIrJ9nnq3BVofi8hrItIlH72E534pIioiHfL0d4uILEjYHk/JVi+dRxG5Kjg2fiYid+bp8ZkEf3NF5OM89fYXkffrjxEicnC2emk09xORfwfHnhdEpE0j9Bo8Vue6v6TRy2t/iRUWhH6UAnOAHkAz4BOgV56a/YADgE9D8NcZOCB43Br4IgR/ApQFj5sCU4BDQvB6HfAkMCEErblAh5A/638APw8eNwO2D3EbWgjsnIdGV+AboEXw/2jgojz0egOfAi0xg6lfB3bPQWerbRm4E/hV8PhXwB156vUE9gQmA31D8tgfaBI8viMEj20SHl8NPJiPXjB/R8xAtnmN2dZT+LsF+GUe20tDmscE203z4P+O+dac8Pw9wG/y9PcacHLw+BRgcgg1fwAcFTz+GXBbI/QaPFbnur+k0ctrf4nTZEMPxsHAV6r6tapWA08Dp+cjqKpvA8vDMKeqVar6UfB4DTAL82WUj6aq6trg36bBlNdoWxHpBgwAHs5HJyqCXyL9gBEAqlqtqitDkj8OmKOq8/LUaQK0EJEmmIZBTtd+B/QE3lfV9apaA7wFnNlYkRTb8umYxhrB3zPy0VPVWar6n8Z6y6D5WlA3wPuYa+nz0Vud8G8rGrG/pDke3Avc0BitDHo5k0LzMuB2Vd0YvGZxnnoAiIgAg4Cn8tRToL6HoS2N3F9SaO4JvB08ngj8uBF6qY7VOe0vqfTy3V/ihA0NjK7Atwn/zyfPL/CoEJHuQB9Mj0O+WqVBF+ViYKKq5qt5H+ZgWZenTj0KvCYiH4pIGOl1PYAlwKNiTuM8LCKtQtAFc5121gfLhlDVBcDdQCVQBaxS1dfykPwU6Cci5SLSEvMLb8cM78mWClWtAnMQBDqGpBsVPwNezldERP4gIt8C5wG/yVPrNGCBqn6Sr68ErgxO4zzSmNNWadgDOFJEpojIWyJyUAiaAEcCi1T1yzx1hgJ3BZ/J3cCwfI1h9pvTgsdnk+M+k3Ssznt/CfPYHydsaGCEFksaJSJSBowBhib9msoJVa1V1f0xv+wOFpHeeXgbCCxW1Q/z9ZXA4ap6AHAycIWI9MtTrwmmO/RvqtoHWIfprswLMUEwpwHP5qnTDvNLZxegC9BKRM7PVU9VZ2FODUwEXsGc+rPt/s6RIyI3Y+oela+Wqt6sqjsGWlfm4aklcDN5NlKS+BuwK7A/poF6TwiaTYB2wCHA/wCjg96HfPkpeTbIAy4Drg0+k2sJeifz5GeY482HmNMS1Y0VCPtYHbZenLChgRFaLGlUiEhTzAY2SlWfD1M7OE0wGTgpD5nDgdNEZC7mFNOxIjIyT1/fBX8XA2Mxp7LyYT4wP6Gn5jlMgyNfTgY+UtVFeeocD3yjqktUdRPwPHBYPoKqOkJVD1DVfpiu4Hx/MdazSEQ6AwR/s+46LyQiciEwEDhPVcP80fAkjeg6b4BdMQ3JT4J9phvwkYh0ylVQVRcFPxrqgIfIf38Bs888H5xSnYrpncx6MGpDBKf/zgKeCcHfhZj9BEwDP++aVXW2qvZX1QMxjaA5jXl/imN1zvtLlMf+OGBDAyO0WNIoCH4xjABmqeqfQtLcQYJR9SLSAvPlNjtXPVUdpqrdVLU7Zv29oao5//oWkVYi0rr+MWbAXl5X5KjqQuBbEdkzmHUc8Hk+mgFh/RqrBA4RkZbBZ34c5pxrzohIx+DvTpiDehg+wewfFwaPLwTGhaQbGiJyEnAjcJqqrg9Bb/eEf08jv/1lpqp2VNXuwT4zHzOYb2Ee/jon/Hsmee4vAf8Ejg3098AMjF6ap+bxwGxVnZ+nDpgfgkcFj48lhAZ0wj5TAvwaeLAR7011rM5pf4ni2B87oh5Fms2EOT/9Baa1enMIek9huik3YQ4el+ShdQTmlM0M4ONgOiVPf/sC0wPNT2nEaO4stI8mz6tIMOMlPgmmz8L4TALd/TG3B56BOXi2y1OvJbAMaBuSv99hvrg+BZ4gGL2fh947mEbUJ8BxOWpstS0D5cAkzAF9EtA+T70zg8cbgUXAqyF4/Aoztqp+n2nMVR8N6Y0JPpcZwAuYwXY56yU9P5fGXUXSkL8ngJmBv/FA5xDWYTNgZFD3R8Cx+dYMPAZcGtJ2eATwYbB9TwEODEHzGsx3wRfA7QRp01nqNXisznV/SaOX1/4Sp8lHhXs8Ho/H4wkdG06ReDwej8fj2cbwDQyPx+PxeDyh4xsYHo/H4/F4Qsc3MDwej8fj8YSOb2B4PB6Px+MJHd/A8Hg8Ho/HEzq+geHxeDwejyd0/j8H0DnVDqdFxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_nb)\n",
    "plt.title('Heatmap of Confusion Matrix Naive Bayes Classifier ', fontsize = 15)\n",
    "sns.heatmap(cm,annot = True,cmap='Wistia' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f74c609e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        18\n",
      "      banana       1.00      1.00      1.00        18\n",
      "   blackgram       1.00      1.00      1.00        22\n",
      "    chickpea       1.00      1.00      1.00        23\n",
      "     coconut       1.00      1.00      1.00        15\n",
      "      coffee       1.00      1.00      1.00        17\n",
      "      cotton       1.00      1.00      1.00        16\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.88      1.00      0.93        21\n",
      " kidneybeans       1.00      1.00      1.00        20\n",
      "      lentil       1.00      1.00      1.00        17\n",
      "       maize       1.00      1.00      1.00        18\n",
      "       mango       1.00      1.00      1.00        21\n",
      "   mothbeans       1.00      1.00      1.00        25\n",
      "    mungbean       1.00      1.00      1.00        17\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        23\n",
      "      papaya       1.00      1.00      1.00        21\n",
      "  pigeonpeas       1.00      1.00      1.00        22\n",
      " pomegranate       1.00      1.00      1.00        23\n",
      "        rice       1.00      0.88      0.94        25\n",
      "  watermelon       1.00      1.00      1.00        17\n",
      "\n",
      "    accuracy                           0.99       440\n",
      "   macro avg       0.99      0.99      0.99       440\n",
      "weighted avg       0.99      0.99      0.99       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#lets print the Classification Report also\n",
    "from sklearn.metrics import classification_report\n",
    "nb=classification_report(y_test, y_pred_nb)\n",
    "print(nb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f610857",
   "metadata": {},
   "source": [
    "## K – Nearest Neighbor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c40fb5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K – Nearest Neighbor Classifier accuracy= 0.9772727272727273\n"
     ]
    }
   ],
   "source": [
    "# K – Nearest Neighbor Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "knn_classifier.fit(x_train, y_train)\n",
    "y_pred_knn = knn_classifier.predict(x_test)\n",
    "knn_acc=accuracy_score(y_test, y_pred_knn)\n",
    "print('K – Nearest Neighbor Classifier accuracy=',knn_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "06b29ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_knn)\n",
    "plt.title('Heatmap of Confusion Matrix Naive Bayes Classifier ', fontsize = 15)\n",
    "sns.heatmap(cm,annot = True,cmap='Wistia' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "067f5c09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       apple       1.00      1.00      1.00        18\n",
      "      banana       1.00      1.00      1.00        18\n",
      "   blackgram       1.00      1.00      1.00        22\n",
      "    chickpea       1.00      1.00      1.00        23\n",
      "     coconut       1.00      1.00      1.00        15\n",
      "      coffee       1.00      1.00      1.00        17\n",
      "      cotton       1.00      0.94      0.97        16\n",
      "      grapes       1.00      1.00      1.00        18\n",
      "        jute       0.79      0.90      0.84        21\n",
      " kidneybeans       0.91      1.00      0.95        20\n",
      "      lentil       1.00      1.00      1.00        17\n",
      "       maize       0.95      1.00      0.97        18\n",
      "       mango       1.00      1.00      1.00        21\n",
      "   mothbeans       1.00      1.00      1.00        25\n",
      "    mungbean       1.00      1.00      1.00        17\n",
      "   muskmelon       1.00      1.00      1.00        23\n",
      "      orange       1.00      1.00      1.00        23\n",
      "      papaya       1.00      1.00      1.00        21\n",
      "  pigeonpeas       1.00      0.91      0.95        22\n",
      " pomegranate       1.00      1.00      1.00        23\n",
      "        rice       0.91      0.80      0.85        25\n",
      "  watermelon       1.00      1.00      1.00        17\n",
      "\n",
      "    accuracy                           0.98       440\n",
      "   macro avg       0.98      0.98      0.98       440\n",
      "weighted avg       0.98      0.98      0.98       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#lets print the Classification Report also\n",
    "from sklearn.metrics import classification_report\n",
    "knn=classification_report(y_test, y_pred_knn)\n",
    "print(knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48c67c68",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}