crop_prediction _based_on_numerical_value.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f4c0de8-c26f-46c1-a9e3-d554ecef2ec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import pickle\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cda4cbd0-1627-4a56-b824-1d998b3e347e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"./data/Crop_recommendation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e10295be-bfa3-463c-9696-0b7c47a72a8e",
   "metadata": {},
   "outputs": [
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       "      <th>N</th>\n",
       "      <th>P</th>\n",
       "      <th>K</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
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       "      <td>rice</td>\n",
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       "      <td>82.320763</td>\n",
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       "      <td>rice</td>\n",
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       "      <td>20.130175</td>\n",
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       "      <td>15</td>\n",
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       "      <td>27.417112</td>\n",
       "      <td>56.636362</td>\n",
       "      <td>6.086922</td>\n",
       "      <td>127.924610</td>\n",
       "      <td>coffee</td>\n",
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       "    <tr>\n",
       "      <th>2197</th>\n",
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       "      <td>24.131797</td>\n",
       "      <td>67.225123</td>\n",
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       "        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\n",
       "...   ...  ..  ..          ...        ...       ...         ...     ...\n",
       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee\n",
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       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee\n",
       "\n",
       "[2200 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79b12e36-737a-462e-9ce7-fdf25e4e1479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2200, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12d5a7eb-986f-4fea-a5f7-f84060f27b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "079fc083-5d60-4739-a7a4-70b3010f459e",
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "181df011-849c-482b-88dd-9f7c0b1e52c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rice', 'maize', 'chickpea', 'kidneybeans', 'pigeonpeas',\n",
       "       'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate',\n",
       "       'banana', 'mango', 'grapes', 'watermelon', 'muskmelon', 'apple',\n",
       "       'orange', 'papaya', 'coconut', 'cotton', 'jute', 'coffee'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b34a5ca-eb49-40fc-8664-7b41ea7037db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "La=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0cf1395b-891e-47d6-9b25-7f1a6b29d24d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new=La.fit_transform(df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dbddea1-d8b4-499a-a173-b9223dec36b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Label_encoded_data']=df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c94285fb-ef73-4e2b-8493-185201187579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 11,  3,  9, 18, 13, 14,  2, 10, 19,  1, 12,  7, 21, 15,  0, 16,\n",
       "       17,  4,  6,  8,  5])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Label_encoded_data.unique()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a25b637-c5ba-4369-8d0d-62ea25101f5a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>27.417112</td>\n",
       "      <td>56.636362</td>\n",
       "      <td>6.086922</td>\n",
       "      <td>127.924610</td>\n",
       "      <td>5</td>\n",
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       "      <td>127.175293</td>\n",
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       "      <td>23.603016</td>\n",
       "      <td>60.396475</td>\n",
       "      <td>6.779833</td>\n",
       "      <td>140.937041</td>\n",
       "      <td>5</td>\n",
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      ],
      "text/plain": [
       "        N   P   K  temperature   humidity        ph    rainfall  \\\n",
       "0      90  42  43    20.879744  82.002744  6.502985  202.935536   \n",
       "1      85  58  41    21.770462  80.319644  7.038096  226.655537   \n",
       "2      60  55  44    23.004459  82.320763  7.840207  263.964248   \n",
       "3      74  35  40    26.491096  80.158363  6.980401  242.864034   \n",
       "4      78  42  42    20.130175  81.604873  7.628473  262.717340   \n",
       "...   ...  ..  ..          ...        ...       ...         ...   \n",
       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507   \n",
       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610   \n",
       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839   \n",
       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293   \n",
       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041   \n",
       "\n",
       "      Label_encoded_data  \n",
       "0                     20  \n",
       "1                     20  \n",
       "2                     20  \n",
       "3                     20  \n",
       "4                     20  \n",
       "...                  ...  \n",
       "2195                   5  \n",
       "2196                   5  \n",
       "2197                   5  \n",
       "2198                   5  \n",
       "2199                   5  \n",
       "\n",
       "[2200 rows x 8 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('label',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bafa9b13-17a6-45af-bebc-c7f23cd78c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = df.drop('label', axis=1).corr()\n",
    "plt.figure(figsize=(8,8))  \n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f82c763-51c2-4a5f-a386-fe6b63073523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/fryMMY42mZmZevjhh+Xv768bbrhBr732mgsrBgAAZQ0BFqVu4cKFqlChgr799lvNmjVLL7zwgl5//XXH8RkzZqh58+batm2bhg4dqiFDhmjfvn1X7C8rK0vp6elOGwAAKL8IsCh1YWFhevHFF1WvXj317t1bjz76qF588UXH8a5du2ro0KGqU6eOnnrqKVWuXFnr1q27Yn9xcXEKDAx0bGFhYaVxGwAAwEUIsCh1LVu2lM1mc3xu1aqVDhw4oNzcXElS48aNHcdsNpuqVaum48ePX7G/sWPHKi0tzbEdPXq05IoHAAAuV8HVBQCX8/DwcPpss9mUl5d3xfZ2u112u72kywIAAGUEM7AodZs3b3b6vGnTJtWtW1fu7u4uqggAAFgJARal7siRI3r88ce1b98+vfvuu5ozZ45Gjhzp6rIAAIBFsIQApa5v3776/fff1aJFC7m7u2vkyJEaNGiQq8sCAAAWQYBFqfPw8NDMmTM1b968fMdSUlLy7du+fXvJFwUAACyDJQQAAACwFAIsAAAALIUlBChViYmJri4BAABYHDOwAAAAsBQCLAAAACyFAAsAAABLIcACAADAUgiwAAAAsBQCLAAAACyFAAsAAABL4T2wKLd2xUYpICDA1WUAAIBixgwsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFL7IAOVWwwmfy83uU+jzUuLvKYFqAABAcWEGFgAAAJZCgAUAAIClEGABAABgKQRYAAAAWAoBFgAAAJZCgAUAAIClEGABAABgKQRYAAAAWAoBFgAAAJZCgAUAAIClEGABAABgKQRYAAAAWAoBFmVGTEyMbDab4uPjnfYvW7ZMNpvNRVUBAICyhgCLMsXLy0vPPfecTp065epSAABAGUWARZnSqVMnVatWTXFxca4uBQAAlFEEWJQp7u7umjZtmubMmaOffvrpus7JyspSenq60wYAAMovAizKnHvvvVdNmjTRhAkTrqt9XFycAgMDHVtYWFgJVwgAAFyJAIsy6bnnntPChQuVlJR0zbZjx45VWlqaYzt69GgpVAgAAFyFAIsy6fbbb1dUVJTGjh17zbZ2u10BAQFOGwAAKL8quLoA4Eri4+PVpEkT1atXz9WlAACAMoQZWJRZjRo1Uu/evTV79mxXlwIAAMoQAizKtEmTJikvL8/VZQAAgDKEJQQoMxISEvLti4iIUFZWVukXAwAAyixmYAEAAGApBFgAAABYCgEWAAAAlkKABQAAgKUQYAEAAGApBFgAAABYCgEWAAAAlkKABQAAgKUQYAEAAGApBFgAAABYCl8li3JrV2yUAgICXF0GAAAoZszAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAAS+GLDFBuNZzwudzsPsXaZ0r8PcXaHwAAKDxmYAEAAGApBFgAAABYCgEWAAAAlkKABQAAgKUQYAEAAGApBFgAAABYCgEWAAAAlkKABQAAgKUQYAEAAGApBFgAAABYCgEWAAAAlkKAxXVJSEhQUFBQoc4xxmjQoEEKDg6WzWbT9u3br3lOSkqKU9vExETZbDadPn260DUDAIDyiQCL6/Lggw9q//79hTpn1apVSkhI0PLly5WamqqGDRuWUHUAAODPpIKrC4DrZWdny9PT86ptvL295e3tXah+k5OTFRoaqtatW/+R8gAAAJwwA/sn1KFDBw0fPlyjRo1S5cqVFRUVpRdeeEGNGjWSr6+vwsLCNHToUGVkZDjOuXwJwcSJE9WkSRMtWrRIERERCgwM1EMPPaQzZ85IkmJiYvToo4/qyJEjstlsioiIkHRhVrZt27YKCgpSpUqV1K1bNyUnJ5fm7QMAAIsjwP5JLVy4UJ6entq4caNeeeUVubm5afbs2dq9e7cWLlyoL7/8Uk8++eRV+0hOTtayZcu0fPlyLV++XOvXr1d8fLwkadasWZo0aZJq1qyp1NRUbdmyRZJ09uxZPf744/ruu++0du1aubm56d5771VeXl6R7yUrK0vp6elOGwAAKL9YQvAnVbduXU2fPt3xuV69eo6fIyIiNGXKFA0ePFhz5869Yh95eXlKSEiQv7+/JKlPnz5au3atpk6dqsDAQPn7+8vd3V3VqlVznPO3v/3NqY8333xTVapU0Z49e4q8RjYuLk6xsbFFOhcAAFgPM7B/UpGRkU6f16xZozvvvFM1atSQv7+/+vTpo5MnTyozM/OKfURERDjCqySFhobq+PHjV73ugQMH9Pe//121a9dWQECAY2nBkSNHinwvY8eOVVpammM7evRokfsCAABlHwH2T8rX19fxc0pKirp166bGjRvrgw8+0NatW/Xyyy9LuvCA15V4eHg4fbbZbNdcCtC9e3f99ttvmj9/vjZv3qzNmzdf8zrXYrfbFRAQ4LQBAIDyiyUE0NatW5WXl6cZM2bIze3C32mWLl1a7Nc5efKk9u3bp/nz56tdu3aSpA0bNhT7dQAAQPlGgIXq1Kmj8+fPa86cOerevbvjwa7iVrFiRVWqVEmvvfaaQkNDdeTIET399NPFfh0AAFC+sYQAuuWWW/TCCy/oueeeU8OGDbV48WLFxcUV+3Xc3Ny0ZMkSbd26VQ0bNtRjjz2mf//738V+HQAAUL7ZjDHG1UUAxSk9PV2BgYEKG7VUbnafYu07Jf6eYu0PAABccPH3d1pa2jWfZ2EGFgAAAJZCgAUAAIClEGABAABgKQRYAAAAWAoBFgAAAJZCgAUAAIClEGABAABgKQRYAAAAWAoBFgAAAJZCgAUAAIClEGABAABgKRVcXQBQUnbFRl3zu5QBAID1MAMLAAAASyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAU3gOLcqvhhM/lZvdxdRnFKiX+HleXAACAyzEDCwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwlGILsKdPny6urgAAAIArKlKAfe655/Tee+85PkdHR6tSpUqqUaOGduzYUWzFWVmHDh00atQoV5dR4iZOnKgmTZq4ugwAAPAnUqQA+8orrygsLEyStHr1aq1evVorV65Uly5dNGbMmGItEK6RnZ1dqtczxignJ6dUrwkAAKypSAH22LFjjgC7fPlyRUdH6+6779aTTz6pLVu2FGuBVhQTE6P169dr1qxZstlsstlsSklJ0a5du9SlSxf5+fkpJCREffr00YkTJxzndejQQY8++qhGjRqlihUrKiQkRPPnz9fZs2fVv39/+fv7q06dOlq5cqXjnMTERNlsNq1YsUKNGzeWl5eXWrZsqV27djnVtGHDBrVr107e3t4KCwvTiBEjdPbsWcfxiIgITZ48WX379lVAQIAGDRokSXrqqad00003ycfHR7Vr19b48eN1/vx5SVJCQoJiY2O1Y8cOx30mJCQoJSVFNptN27dvd/R/+vRp2Ww2JSYmOtW9cuVKRUZGym63a8OGDcrLy1NcXJxq1aolb29v3XLLLXr//feL+48IAABYWJECbMWKFXX06FFJ0qpVq9SpUydJF2bRcnNzi686i5o1a5ZatWqlgQMHKjU1VampqfL391fHjh3VtGlTfffdd1q1apV+/fVXRUdHO527cOFCVa5cWd9++60effRRDRkyRA888IBat26t77//Xnfffbf69OmjzMxMp/PGjBmjGTNmaMuWLapSpYq6d+/uCJrJycnq3Lmz/va3v+mHH37Qe++9pw0bNmj48OFOfTz//PO65ZZbtG3bNo0fP16S5O/vr4SEBO3Zs0ezZs3S/Pnz9eKLL0qSHnzwQT3xxBO6+eabHff54IMPFmqsnn76acXHxyspKUmNGzdWXFyc3nrrLb3yyivavXu3HnvsMf3jH//Q+vXrr9hHVlaW0tPTnTYAAFB+VSjKSffdd5969eqlunXr6uTJk+rSpYskadu2bapTp06xFmhFgYGB8vT0lI+Pj6pVqyZJmjJlipo2bapp06Y52r355psKCwvT/v37ddNNN0mSbrnlFj3zzDOSpLFjxyo+Pl6VK1fWwIEDJUnPPvus5s2bpx9++EEtW7Z09DVhwgTdddddki6E4Jo1a+qjjz5SdHS04uLi1Lt3b8ea3Lp162r27Nlq37695s2bJy8vL0lSx44d9cQTTzjdy8VapAuztKNHj9aSJUv05JNPytvbW35+fqpQoYLjPgtr0qRJjrqzsrI0bdo0rVmzRq1atZIk1a5dWxs2bNCrr76q9u3bF9hHXFycYmNji3R9AABgPUUKsC+++KIiIiJ09OhRTZ8+XX5+fpKk1NRUDR06tFgLLC927NihdevWOcbqUsnJyY4A27hxY8d+d3d3VapUSY0aNXLsCwkJkSQdP37cqY+LgU+SgoODVa9ePSUlJTmu/cMPP2jx4sWONsYY5eXl6fDhw2rQoIEkqXnz5vlqe++99zR79mwlJycrIyNDOTk5CggIKPT9X8ml1zx48KAyMzMdgfai7OxsNW3a9Ip9jB07Vo8//rjjc3p6umOJCwAAKH+KFGA9PDw0evTofPsfe+yxP1xQeZWRkaHu3bvrueeey3csNDTU8bOHh4fTMZvN5rTPZrNJkvLy8gp17X/+858aMWJEvmM33HCD42dfX1+nY99884169+6t2NhYRUVFKTAwUEuWLNGMGTOuej03twsrU4wxjn0XlzNc7tJrZmRkSJJWrFihGjVqOLWz2+1XvJ7dbr/qcQAAUL4UKcBK0qJFi/Tqq6/q0KFD+uabbxQeHq6ZM2eqVq1a6tGjR3HWaEmenp5O64GbNWumDz74QBEREapQocjDfkWbNm1yhNFTp05p//79jpnVZs2aac+ePYVe3vH1118rPDxc48aNc+z78ccfndpcfp+SVKVKFUkXZuQvzpxe+kDXlfzlL3+R3W7XkSNHrrhcAAAAoEgPcc2bN0+PP/64unTpotOnTzsCTFBQkGbOnFmc9VlWRESENm/erJSUFJ04cULDhg3Tb7/9pr///e/asmWLkpOT9fnnn6t///7F8uDbpEmTtHbtWu3atUsxMTGqXLmyevbsKenCmwS+/vprDR8+XNu3b9eBAwf08ccf53uI63J169bVkSNHtGTJEiUnJ2v27Nn66KOP8t3n4cOHtX37dp04cUJZWVny9vZWy5YtHQ9nrV+/3mkt7ZX4+/tr9OjReuyxx7Rw4UIlJyfr+++/15w5c7Rw4cIijw0AAChfihRg58yZo/nz52vcuHFyd3d37G/evLl27txZbMVZ2ejRo+Xu7q6//OUvqlKlirKzs7Vx40bl5ubq7rvvVqNGjTRq1CgFBQU5/pf7HxEfH6+RI0cqMjJSx44d06effipPT09JF9bVrl+/Xvv371e7du3UtGlTPfvss6pevfpV+/zrX/+qxx57TMOHD1eTJk309ddfO95OcNHf/vY3de7cWXfccYeqVKmid999V9KFB9RycnIUGRmpUaNGacqUKdd1H5MnT9b48eMVFxenBg0aqHPnzlqxYoVq1apVhFEBAADlkc1culDxOnl7e2vv3r0KDw+Xv7+/duzYodq1a+vAgQNq3Lixfv/995KoFQVITEzUHXfcoVOnTikoKMjV5ZQJ6enpCgwMVNiopXKz+7i6nGKVEn+Pq0sAAKBEXPz9nZaWds0Hxos09VerVq0C1zSuWrXKse4SAAAAKAlFepro8ccf17Bhw3Tu3DkZY/Ttt9/q3XffVVxcnF5//fXirhEAAABwKFKAfeSRR+Tt7a1nnnlGmZmZ6tWrl6pXr65Zs2bpoYceKu4acRUdOnRQEVaBAAAAWFahA2xOTo7eeecdRUVFqXfv3srMzFRGRoaqVq1aEvUBAAAATgq9BrZChQoaPHiwzp07J0ny8fEhvAIAAKDUFOkhrhYtWmjbtm3FXQsAAABwTUVaAzt06FA98cQT+umnnxQZGZnvK0gbN25cLMUBAAAAlytSgL34oNaIESMc+2w2m4wxstlsxfLNUgAAAEBBihRgDx8+XNx1AAAAANelSAE2PDy8uOsAAAAArkuRAuxbb7111eN9+/YtUjEAAADAtdhMEd6CX7FiRafP58+fV2Zmpjw9PeXj46Pffvut2AoECqsw36UMAADKhsL8/i7Sa7ROnTrltGVkZGjfvn1q27at3n333SIVDQAAAFyPIgXYgtStW1fx8fEaOXJkcXUJAAAA5FNsAVa68C1dv/zyS3F2CQAAADgp0kNcn3zyidNnY4xSU1P10ksvqU2bNsVSGAAAAFCQIgXYnj17On222WyqUqWKOnbsqBkzZhRHXQAAAECBihRg8/LyirsOAAAA4LoUaQ3spEmTlJmZmW//77//rkmTJv3hogAAAIArKdJ7YN3d3ZWamqqqVas67T958qSqVq2q3NzcYisQKKyL75ELG7VUbnYfV5fzp5ASf4+rSwAAWFyJvwfWGCObzZZv/44dOxQcHFyULgEAAIDrUqg1sBUrVpTNZpPNZtNNN93kFGJzc3OVkZGhwYMHF3uRAAAAwEWFCrAzZ86UMUYPP/ywYmNjFRgY6Djm6empiIgItWrVqtiLBAAAAC4qVIDt16+fJKlWrVpq3bq1PDw8SqQoAAAA4EqK9Bqt9u3bO34+d+6csrOznY5fa+EtAAAAUFRFeogrMzNTw4cPV9WqVeXr66uKFSs6bQAAAEBJKVKAHTNmjL788kvNmzdPdrtdr7/+umJjY1W9enW99dZbxV0jAAAA4FCkJQSffvqp3nrrLXXo0EH9+/dXu3btVKdOHYWHh2vx4sXq3bt3cdcJAAAASCriDOxvv/2m2rVrS7qw3vW3336TJLVt21ZfffVV8VUHAAAAXKZIAbZ27do6fPiwJKl+/fpaunSppAszs0FBQcVWHAAAAHC5IgXY/v37a8eOHZKkp59+Wi+//LK8vLz02GOPacyYMcVaIAAAAHCpIgXYxx57TCNGjJAkderUSXv37tU777yjbdu2aeTIkcVaIK6uQ4cOGjVqVJm8bkREhGbOnOn4bLPZtGzZshKtCwAAlH9FeojrUufOnVN4eLjCw8OLox5YxIcffljoL7JITU11vGYtJSVFtWrV0rZt29SkSZMSqBAAAJRXRZqBzc3N1eTJk1WjRg35+fnp0KFDkqTx48frjTfeKNYCUTYFBwfL39+/UOdUq1ZNdru9hCoCAAB/FkUKsFOnTlVCQoKmT58uT09Px/6GDRvq9ddfL7bicH3y8vL05JNPKjg4WNWqVdPEiRMlXZjltNls2r59u6Pt6dOnZbPZlJiYKElKTEyUzWbT559/rqZNm8rb21sdO3bU8ePHtXLlSjVo0EABAQHq1auXMjMzHf1cvoTg+PHj6t69u7y9vVWrVi0tXrw4X52XLiGoVauWJKlp06ay2Wzq0KGDvvrqK3l4eOjYsWNO540aNUrt2rX74wMFAADKhSIF2LfeekuvvfaaevfuLXd3d8f+W265RXv37i224nB9Fi5cKF9fX23evFnTp0/XpEmTtHr16kL1MXHiRL300kv6+uuvdfToUUVHR2vmzJl65513tGLFCn3xxReaM2fOFc+PiYnR0aNHtW7dOr3//vuaO3eujh8/fsX23377rSRpzZo1Sk1N1Ycffqjbb79dtWvX1qJFixztzp8/r8WLF+vhhx++Yl9ZWVlKT0932gAAQPlVpAD7888/q06dOvn25+Xl6fz583+4KBRO48aNNWHCBNWtW1d9+/ZV8+bNtXbt2kL1MWXKFLVp00ZNmzbVgAEDtH79es2bN09NmzZVu3btdP/992vdunUFnrt//36tXLlS8+fPV8uWLRUZGak33nhDv//++xWvV6VKFUlSpUqVVK1aNQUHB0uSBgwYoAULFjjaffrppzp37pyio6Ov2FdcXJwCAwMdW1hYWKHuHQAAWEuRAuxf/vIX/fe//823//3331fTpk3/cFEonMaNGzt9Dg0Nvers57X6CAkJkY+Pj+PLKi7uu1KfSUlJqlChgiIjIx376tevX6R3AsfExOjgwYPatGmTJCkhIUHR0dHy9fW94jljx45VWlqaYzt69GihrwsAAKyjSG8hePbZZ9WvXz/9/PPPysvL04cffqh9+/bprbfe0vLly4u7RlzD5W8DsNlsysvLk5vbhb+fGGMcx640Q35pHzab7Yp9lrSqVauqe/fuWrBggWrVqqWVK1c61uteid1u5+EwAAD+RAo1A3vo0CEZY9SjRw99+umnWrNmjXx9ffXss88qKSlJn376qe66666SqhWFdPF/06empjr2XfpAV3GpX7++cnJytHXrVse+ffv26fTp01c85+LDf7m5ufmOPfLII3rvvff02muv6cYbb1SbNm2KvWYAAGBdhZqBrVu3rlJTU1W1alW1a9dOwcHB2rlzp0JCQkqqPvwB3t7eatmypeLj41WrVi0dP35czzzzTLFfp169eurcubP++c9/at68eapQoYJGjRolb2/vK55TtWpVeXt7a9WqVapZs6a8vLwUGBgoSYqKilJAQICmTJmiSZMmFXu9AADA2go1A3vp/4qWpJUrV+rs2bPFWhCK15tvvqmcnBxFRkZq1KhRmjJlSolcZ8GCBapevbrat2+v++67T4MGDVLVqlWv2L5ChQqaPXu2Xn31VVWvXl09evRwHHNzc1NMTIxyc3PVt2/fEqkXAABYl81cnkqvws3NTceOHXMEE39/f+3YscPpYR+gOAwYMED/+9//9MknnxT63PT09AtvIxi1VG52nxKoDpdLib/H1SUAACzu4u/vtLQ0BQQEXLVtoZYQ2Gw22Wy2fPuA4pKWlqadO3fqnXfeKVJ4BQAA5V+hAqwxRjExMY4nvs+dO6fBgwfne8XRhx9+WHwV4k+lR48e+vbbbzV48GAeCAQAAAUqVIDt16+f0+d//OMfxVoMcK1XZgEAABQqwF76DUkAAACAKxTpm7gAAAAAVyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALCUQr0HFrCSXbFR1/wuZQAAYD3MwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIX3wKLcajjhc7nZfVxdBqCU+HtcXQIAlCvMwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwKLMiYmJUc+ePZ32vf/++/Ly8tKMGTNcUxQAACgzKri6AOBaXn/9dQ0bNkyvvPKK+vfv7+pyAACAizEDizJt+vTpevTRR7VkyRLCKwAAkMQMLMqwp556SnPnztXy5ct15513urocAABQRhBgUSatXLlSH3/8sdauXauOHTtetW1WVpaysrIcn9PT00u6PAAA4EIsIUCZ1LhxY0VERGjChAnKyMi4atu4uDgFBgY6trCwsFKqEgAAuAIBFmVSjRo1lJiYqJ9//lmdO3fWmTNnrth27NixSktLc2xHjx4txUoBAEBpI8CizAoPD9f69et17Nixq4ZYu92ugIAApw0AAJRfBFiUaWFhYUpMTNTx48cVFRXF+lYAAECARdlXs2ZNJSYm6sSJE4RYAADAWwhQ9iQkJOTbV6NGDe3fv7/0iwEAAGUOM7AAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEup4OoCgJKyKzZKAQEBri4DAAAUM2ZgAQAAYCkEWAAAAFgKARYAAACWQoAFAACApRBgAQAAYCkEWAAAAFgKARYAAACWwntgUW41nPC53Ow+ri4DwGVS4u9xdQkALI4ZWAAAAFgKARYAAACWQoAFAACApRBgAQAAYCkEWAAAAFgKARYAAACWQoAFAACApRBgAQAAYCkEWAAAAFgKARYAAACWQoAFAACApRBgAQAAYCkEWJQ5MTExstlsstls8vT0VJ06dTRp0iTl5OS4ujQAAFAGVHB1AUBBOnfurAULFigrK0ufffaZhg0bJg8PD40dO9bVpQEAABdjBhZlkt1uV7Vq1RQeHq4hQ4aoU6dO+uSTT1xdFgAAKAMIsLAEb29vZWdnu7oMAABQBhBgUaYZY7RmzRp9/vnn6tixY4FtsrKylJ6e7rQBAIDyizWwKJOWL18uPz8/nT9/Xnl5eerVq5cmTpxYYNu4uDjFxsaWboEAAMBlmIFFmXTHHXdo+/btOnDggH7//XctXLhQvr6+BbYdO3as0tLSHNvRo0dLuVoAAFCamIFFmeTr66s6depcV1u73S673V7CFQEAgLKCGVgAAABYCgEWAAAAlsISApQ5CQkJri4BAACUYczAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAspYKrCwBKyq7YKAUEBLi6DAAAUMyYgQUAAIClEGABAABgKQRYAAAAWAoBFgAAAJZCgAUAAIClEGABAABgKQRYAAAAWArvgUW51XDC53Kz+7i6DAAAypWU+HtcXQIzsAAAALAWAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIsG2ATEhIUFBT0h/ux2WxatmzZH+7HlTp06KBRo0b9oT7+6HhOnDhRTZo0+UM1AAAAXA+XBtiYmBj17NnTlSXAhcrDXx4AAEDps+wMLAAAAP6cymyAfeGFF9SoUSP5+voqLCxMQ4cOVUZGRr52y5YtU926deXl5aWoqCgdPXrU6fjHH3+sZs2aycvLS7Vr11ZsbKxycnKKVNPRo0cVHR2toKAgBQcHq0ePHkpJSXEcvzij/Pzzzys0NFSVKlXSsGHDdP78eUebrKwsPfXUUwoLC5PdbledOnX0xhtvOI6vX79eLVq0kN1uV2hoqJ5++mmnes+ePau+ffvKz89PoaGhmjFjRr46s7KyNHr0aNWoUUO+vr667bbblJiY6NQmISFBN9xwg3x8fHTvvffq5MmThRqL+Ph4hYSEyN/fXwMGDNC5c+ecjm/ZskV33XWXKleurMDAQLVv317ff/+943hERIQk6d5775XNZnN8Tk5OVo8ePRQSEiI/Pz/deuutWrNmTaFqAwAA5VuZDbBubm6aPXu2du/erYULF+rLL7/Uk08+6dQmMzNTU6dO1VtvvaWNGzfq9OnTeuihhxzH//vf/6pv374aOXKk9uzZo1dffVUJCQmaOnVqoes5f/68oqKi5O/vr//+97/auHGj/Pz81LlzZ2VnZzvarVu3TsnJyVq3bp0WLlyohIQEJSQkOI737dtX7777rmbPnq2kpCS9+uqr8vPzkyT9/PPP6tq1q2699Vbt2LFD8+bN0xtvvKEpU6Y4zh8zZozWr1+vjz/+WF988YUSExOdgqEkDR8+XN98842WLFmiH374QQ888IA6d+6sAwcOSJI2b96sAQMGaPjw4dq+fbvuuOMOp2tcy9KlSzVx4kRNmzZN3333nUJDQzV37lynNmfOnFG/fv20YcMGbdq0SXXr1lXXrl115swZSRcCriQtWLBAqampjs8ZGRnq2rWr1q5dq23btqlz587q3r27jhw5csV6srKylJ6e7rQBAIDyy2aMMa66eExMjE6fPn1d6yDff/99DR48WCdOnJB0YQaxf//+2rRpk2677TZJ0t69e9WgQQNt3rxZLVq0UKdOnXTnnXdq7Nixjn7efvttPfnkk/rll18kXViH+dFHH11zLe7bb7+tKVOmKCkpSTabTZKUnZ2toKAgLVu2THfffbdiYmKUmJio5ORkubu7S5Kio6Pl5uamJUuWaP/+/apXr55Wr16tTp065bvGuHHj9MEHHzhdY+7cuXrqqaeUlpamzMxMVapUSW+//bYeeOABSdJvv/2mmjVratCgQZo5c6aOHDmi2rVr68iRI6pevbqj706dOqlFixaaNm2aevXqpbS0NK1YscJx/KGHHtKqVat0+vTpa/5ZtG7dWk2bNtXLL7/s2NeyZUudO3dO27dvL/CcvLw8BQUF6Z133lG3bt0kXf/YN2zYUIMHD9bw4cMLPD5x4kTFxsbm2x82aqnc7D7XvB8AAHD9UuLvKZF+09PTFRgYqLS0NAUEBFy1bZmdgV2zZo3uvPNO1ahRQ/7+/urTp49OnjypzMxMR5sKFSro1ltvdXyuX7++goKClJSUJEnasWOHJk2aJD8/P8c2cOBApaamOvVzPXbs2KGDBw/K39/f0VdwcLDOnTun5ORkR7ubb77ZEV4lKTQ0VMePH5ckbd++Xe7u7mrfvn2B10hKSlKrVq0c4VWS2rRpo4yMDP30009KTk5Wdna2I7BLUnBwsOrVq+f4vHPnTuXm5uqmm25yuu/169c76kxKSnLqQ5JatWp13WNxPef/+uuvGjhwoOrWravAwEAFBAQoIyPjqjOp0oUZ2NGjR6tBgwYKCgqSn5+fkpKSrnre2LFjlZaW5tguX0YCAADKlwquLqAgKSkp6tatm4YMGaKpU6cqODhYGzZs0IABA5SdnS0fn+ubVcvIyFBsbKzuu+++fMe8vLwKVVNGRoYiIyO1ePHifMeqVKni+NnDw8PpmM1mU15eniTJ29u7UNcsioyMDLm7u2vr1q1OQVqSY6lCaejXr59OnjypWbNmKTw8XHa7Xa1atXJablGQ0aNHa/Xq1Xr++edVp04deXt76/7777/qeXa7XXa7vbhvAQAAlFFlMsBu3bpVeXl5mjFjhtzcLkwSL126NF+7nJwcfffdd2rRooUkad++fTp9+rQaNGggSWrWrJn27dunOnXq/OGamjVrpvfee09Vq1a95rT2lTRq1Eh5eXlav359gUsIGjRooA8++EDGGMcs7MaNG+Xv76+aNWsqODhYHh4e2rx5s2644QZJ0qlTp7R//37HrG7Tpk2Vm5ur48ePq127dgXWcXGZxaU2bdp03fdx8fy+ffte8fyNGzdq7ty56tq1q6QLD8BdXP5xkYeHh3Jzc/OdFxMTo3vvvVfShUB+6YNyAAAALl9CkJaWpu3btzttlStX1vnz5zVnzhwdOnRIixYt0iuvvJLvXA8PDz366KPavHmztm7dqpiYGLVs2dIRaJ999lm99dZbio2N1e7du5WUlKQlS5bomWeeKXSdvXv3VuXKldWjRw/997//1eHDh5WYmKgRI0bop59+uq4+IiIi1K9fPz388MNatmyZo4+L4Xzo0KE6evSoHn30Ue3du1cff/yxJkyYoMcff1xubm7y8/PTgAEDNGbMGH355ZfatWuXYmJiHCFfkm666Sb17t1bffv21YcffqjDhw/r22+/VVxcnGPN64gRI7Rq1So9//zzOnDggF566SWtWrXqusdi5MiRevPNN7VgwQLt379fEyZM0O7du53a1K1bV4sWLVJSUpI2b96s3r1755uBjoiI0Nq1a3Xs2DGdOnXKcd6HH36o7du3a8eOHerVq5djBhsAAEAqAwE2MTFRTZs2ddoWLVqkF154Qc8995waNmyoxYsXKy4uLt+5Pj4+euqpp9SrVy+1adNGfn5+eu+99xzHo6KitHz5cn3xxRe69dZb1bJlS7344osKDw8vdJ0+Pj766quvdMMNN+i+++5TgwYNHK+PKsyM7Lx583T//fdr6NChql+/vgYOHKizZ89KkmrUqKHPPvtM3377rW655RYNHjxYAwYMcArc//73v9WuXTt1795dnTp1Utu2bRUZGel0jQULFqhv37564oknVK9ePfXs2VNbtmxxzNq2bNlS8+fP16xZs3TLLbfoiy++KFSof/DBBzV+/Hg9+eSTioyM1I8//qghQ4Y4tXnjjTd06tQpNWvWTH369NGIESNUtWpVpzYzZszQ6tWrFRYWpqZNm0q68Pq0ihUrqnXr1urevbuioqLUrFmz664NAACUfy59CwFQEi4+xchbCAAAKH68hQAAAAAoJALs/zdt2jSn105dunXp0sXV5ZWqm2+++YpjUdBbGAAAAEpTmXwLgSsMHjxY0dHRBR4rjddflSWfffaZ09ffXiokJKSUqwEAAHBGgP3/goODFRwc7OoyyoSiPOQGAABQWlhCAAAAAEshwAIAAMBSCLAAAACwFAIsAAAALIUACwAAAEshwAIAAMBSeI0Wyq1dsVHX/Co6AABgPczAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAshQALAAAASyHAAgAAwFIIsAAAALAUAiwAAAAspYKrCwCKmzFGkpSenu7iSgAAwPW6+Hv74u/xqyHAotw5efKkJCksLMzFlQAAgMI6c+aMAgMDr9qGAItyJzg4WJJ05MiRa/4LgOKVnp6usLAwHT16VAEBAa4u50+DcXcdxt41GHfXKcmxN8bozJkzql69+jXbEmBR7ri5XVjaHRgYyH/YXCQgIICxdwHG3XUYe9dg3F2npMb+eieeeIgLAAAAlkKABQAAgKUQYFHu2O12TZgwQXa73dWl/Okw9q7BuLsOY+8ajLvrlJWxt5nreVcBAAAAUEYwAwsAAABLIcACAADAUgiwAAAAsBQCLAAAACyFAAtLevnllxURESEvLy/ddttt+vbbb6/a/j//+Y/q168vLy8vNWrUSJ999lkpVVr+FGbs58+fr3bt2qlixYqqWLGiOnXqdM0/KxSssP/MX7RkyRLZbDb17NmzZAsspwo77qdPn9awYcMUGhoqu92um266if/eFFFhx37mzJmqV6+evL29FRYWpscee0znzp0rpWrLh6+++krdu3dX9erVZbPZtGzZsmuek5iYqGbNmslut6tOnTpKSEgo8TolSQawmCVLlhhPT0/z5ptvmt27d5uBAweaoKAg8+uvvxbYfuPGjcbd3d1Mnz7d7NmzxzzzzDPGw8PD7Ny5s5Qrt77Cjn2vXr3Myy+/bLZt22aSkpJMTEyMCQwMND/99FMpV25thR33iw4fPmxq1Khh2rVrZ3r06FE6xZYjhR33rKws07x5c9O1a1ezYcMGc/jwYZOYmGi2b99eypVbX2HHfvHixcZut5vFixebw4cPm88//9yEhoaaxx57rJQrt7bPPvvMjBs3znz44YdGkvnoo4+u2v7QoUPGx8fHPP7442bPnj1mzpw5xt3d3axatarEayXAwnJatGhhhg0b5vicm5trqlevbuLi4gpsHx0dbe655x6nfbfddpv55z//WaJ1lkeFHfvL5eTkGH9/f7Nw4cKSKrFcKsq45+TkmNatW5vXX3/d9OvXjwBbBIUd93nz5pnatWub7Ozs0iqx3Crs2A8bNsx07NjRad/jjz9u2rRpU6J1lmfXE2CffPJJc/PNNzvte/DBB01UVFQJVnYBSwhgKdnZ2dq6das6derk2Ofm5qZOnTrpm2++KfCcb775xqm9JEVFRV2xPQpWlLG/XGZmps6fP6/g4OCSKrPcKeq4T5o0SVWrVtWAAQNKo8xypyjj/sknn6hVq1YaNmyYQkJC1LBhQ02bNk25ubmlVXa5UJSxb926tbZu3epYZnDo0CF99tln6tq1a6nU/Gflyt+vFUr8CkAxOnHihHJzcxUSEuK0PyQkRHv37i3wnGPHjhXY/tixYyVWZ3lUlLG/3FNPPaXq1avn+w8erqwo475hwwa98cYb2r59eylUWD4VZdwPHTqkL7/8Ur1799Znn32mgwcPaujQoTp//rwmTJhQGmWXC0UZ+169eunEiRNq27atjDHKycnR4MGD9a9//as0Sv7TutLv1/T0dP3+++/y9vYusWszAwugVMTHx2vJkiX66KOP5OXl5epyyq0zZ86oT58+mj9/vipXruzqcv5U8vLyVLVqVb322muKjIzUgw8+qHHjxumVV15xdWnlXmJioqZNm6a5c+fq+++/14cffqgVK1Zo8uTJri4NJYQZWFhK5cqV5e7url9//dVp/6+//qpq1aoVeE61atUK1R4FK8rYX/T8888rPj5ea9asUePGjUuyzHKnsOOenJyslJQUde/e3bEvLy9PklShQgXt27dPN954Y8kWXQ4U5Z/30NBQeXh4yN3d3bGvQYMGOnbsmLKzs+Xp6VmiNZcXRRn78ePHq0+fPnrkkUckSY0aNdLZs2c1aNAgjRs3Tm5uzNeVhCv9fg0ICCjR2VeJGVhYjKenpyIjI7V27VrHvry8PK1du1atWrUq8JxWrVo5tZek1atXX7E9ClaUsZek6dOna/LkyVq1apWaN29eGqWWK4Ud9/r162vnzp3avn27Y/vrX/+qO+64Q9u3b1dYWFhplm9ZRfnnvU2bNjp48KDjLwyStH//foWGhhJeC6EoY5+ZmZkvpF78i4QxpuSK/ZNz6e/XEn9MDChmS5YsMXa73SQkJJg9e/aYQYMGmaCgIHPs2DFjjDF9+vQxTz/9tKP9xo0bTYUKFczzzz9vkpKSzIQJE3iNVhEVduzj4+ONp6enef/9901qaqpjO3PmjKtuwZIKO+6X4y0ERVPYcT9y5Ijx9/c3w4cPN/v27TPLly83VatWNVOmTHHVLVhWYcd+woQJxt/f37z77rvm0KFD5osvvjA33nijiY6OdtUtWNKZM2fMtm3bzLZt24wk88ILL5ht27aZH3/80RhjzNNPP2369OnjaH/xNVpjxowxSUlJ5uWXX+Y1WsDVzJkzx9xwww3G09PTtGjRwmzatMlxrH379qZfv35O7ZcuXWpuuukm4+npaW6++WazYsWKUq64/CjM2IeHhxtJ+bYJEyaUfuEWV9h/5i9FgC26wo77119/bW677TZjt9tN7dq1zdSpU01OTk4pV10+FGbsz58/byZOnGhuvPFG4+XlZcLCwszQoUPNqVOnSr9wC1u3bl2B/82+ONb9+vUz7du3z3dOkyZNjKenp6ldu7ZZsGBBqdRqM4a5dQAAAFgHa2ABAABgKQRYAAAAWAoBFgAAAJZCgAUAAIClEGABAABgKQRYAAAAWAoBFgAAAJZCgAUAAIClEGABwAJiYmJks9nybQcPHiyW/hMSEhQUFFQsfRVVTEyMevbs6dIariYlJUU2m03bt293dSnAn14FVxcAALg+nTt31oIFC5z2ValSxUXVXNn58+fl4eHh6jKKVXZ2tqtLAHAJZmABwCLsdruqVavmtLm7u0uSPv74YzVr1kxeXl6qXbu2YmNjlZOT4zj3hRdeUKNGjeTr66uwsDANHTpUGRkZkqTExET1799faWlpjpndiRMnSpJsNpuWLVvmVEdQUJASEhIk/d+s5Hvvvaf27dvLy8tLixcvliS9/vrratCggby8vFS/fn3NnTu3UPfboUMHPfrooxo1apQqVqyokJAQzZ8/X2fPnlX//v3l7++vOnXqaOXKlY5zEhMTZbPZtGLFCjVu3FheXl5q2bKldu3a5dT3Bx98oJtvvll2u10RERGaMWOG0/GIiAhNnjxZffv2VUBAgAYNGqRatWpJkpo2bSqbzaYOHTpIkrZs2aK77rpLlStXVmBgoNq3b6/vv//eqT+bzabXX39d9957r3x8fFS3bl198sknTm12796tbt26KSAgQP7+/mrXrp2Sk5Mdx//oeALligEAlHn9+vUzPXr0KPDYV199ZQICAkxCQoJJTk42X3zxhYmIiDATJ050tHnxxRfNl19+aQ4fPmzWrl1r6tWrZ4YMGWKMMSYrK8vMnDnTBAQEmNTUVJOammrOnDljjDFGkvnoo4+crhcYGGgWLFhgjDHm8OHDRpKJiIgwH3zwgTl06JD55ZdfzNtvv21CQ0Md+z744AMTHBxsEhISrvse27dvb/z9/c3kyZPN/v37zeTJk427u7vp0qWLee2118z+/fvNkCFDTKVKlczZs2eNMcasW7fOSDINGjQwX3zxhfnhhx9Mt27dTEREhMnOzjbGGPPdd98ZNzc3M2nSJLNv3z6zYMEC4+3t7bgnY4wJDw83AQEB5vnnnzcHDx40Bw8eNN9++62RZNasWWNSU1PNyZMnjTHGrF271ixatMgkJSWZPXv2mAEDBpiQkBCTnp7u6E+SqVmzpnnnnXfMgQMHzIgRI4yfn5+jj59++skEBweb++67z2zZssXs27fPvPnmm2bv3r3GGFOk8QTKMwIsAFhAv379jLu7u/H19XVs999/vzHGmDvvvNNMmzbNqf2iRYtMaGjoFfv7z3/+YypVquT4vGDBAhMYGJiv3fUG2JkzZzq1ufHGG80777zjtG/y5MmmVatWV73HywNs27ZtHZ9zcnKMr6+v6dOnj2NfamqqkWS++eYbY8z/BdglS5Y42pw8edJ4e3ub9957zxhjTK9evcxdd93ldO0xY8aYv/zlL47P4eHhpmfPnk5tLt7rtm3brngPxhiTm5tr/P39zaeffurYJ8k888wzjs8ZGRlGklm5cqUxxpixY8eaWrVqOUL25YoynkB5xhpYALCIO+64Q/PmzXN89vX1lSTt2LFDGzdu1NSpUx3HcnNzde7cOWVmZsrHx0dr1qxRXFyc9u7dq/T0dOXk5Dgd/6OaN2/u+Pns2bNKTk7WgAEDNHDgQMf+nJwcBQYGFqrfxo0bO352d3dXpUqV1KhRI8e+kJAQSdLx48edzmvVqpXj5+DgYNWrV09JSUmSpKSkJPXo0cOpfZs2bTRz5kzl5uY6lmVcek9X8+uvv+qZZ55RYmKijh8/rtzcXGVmZurIkSNXvBdfX18FBAQ46t6+fbvatWtX4Nrh4hxPoLwgwAKARfj6+qpOnTr59mdkZCg2Nlb33XdfvmNeXl5KSUlRt27dNGTIEE2dOlXBwcHasGGDBgwYoOzs7KsGWJvNJmOM077z588XWNul9UjS/Pnzddtttzm1uxgOr9flgc5msznts9lskqS8vLxC9Xs9Lr2nq+nXr59OnjypWbNmKTw8XHa7Xa1atcr34FdB93Kxbm9v7yv2X5zjCZQXBFgAsLhmzZpp3759BYZbSdq6davy8vI0Y8YMubldeHZ36dKlTm08PT2Vm5ub79wqVaooNTXV8fnAgQPKzMy8aj0hISGqXr26Dh06pN69exf2dorFpk2bdMMNN0iSTp06pf3796tBgwaSpAYNGmjjxo1O7Tdu3KibbrrpqoHQ09NTkvKN08aNGzV37lx17dpVknT06FGdOHGiUPU2btxYCxcuLPANDmVhPIGyhgALABb37LPPqlu3brrhhht0//33y83NTTt27NCuXbs0ZcoU1alTR+fPn9ecOXPUvXt3bdy4Ua+88opTHxEREcrIyNDatWt1yy23yMfHRz4+PurYsaNeeukltWrVSrm5uXrqqaeu6xVZsbGxGjFihAIDA9W5c2dlZWXpu+++06lTp/T444+X1FA4TJo0SZUqVVJISIjGjRunypUrO94x+8QTT+jWW2/V5MmT9eCDD+qbb77RSy+9dM2n+qtWrSpvb2+tWrVKNWvWlJeXlwIDA1W3bl0tWrRIzZs3V3p6usaMGXPVGdWCDB8+XHPmzNFDDz2ksWPHKjAwUJs2bVKLFi1Ur149l48nUNbwGi0AsLioqCgtX75cX3zxhW699Va1bNlSL774osLDwyVJt9xyi1544QU999xzatiwoRYvXqy4uDinPlq3bq3BgwfrwQcfVJUqVTR9+nRJ0owZMxQWFqZ27dqpV69eGj169HWtmX3kkUf0+uuva8GCBWrUqJHat2+vhIQEx6uoSlp8fLxGjhypyMhIHTt2TJ9++qljBrVZs2ZaunSplixZooYNG+rZZ5/VpEmTFBMTc9U+K1SooNmzZ+vVV19V9erVHeto33jjDZ06dUrNmjVTnz59NGLECFWtWrVQ9VaqVElffvmlMjIy1L59e0VGRmr+/PmOvyy4ejyBssZmLl/cBACARSUmJuqOO+7QqVOnXP7NYgBKDjOwAAAAsBQCLAAAACyFJQQAAACwFGZgAQAAYCkEWAAAAFgKARYAAACWQoAFAACApRBgAQAAYCkEWAAAAFgKARYAAACWQoAFAACApRBgAQAAYCn/D9+hC75+3oIKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "feature_importance = corr_matrix['Label_encoded_data'].abs().sort_values(ascending=False)\n",
    "plt.barh(feature_importance.index, feature_importance.values)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Feature Importance')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9723c95-813c-4dca-8905-da36e1f095a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "x=df.drop(['Label_encoded_data','label'],axis=1)\n",
    "y=df.iloc[:,-1]\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7937ef8a-0efb-44ab-b158-466ca0193edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Label_encoded_data</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",
       "      <td>20</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",
       "      <td>20</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",
       "      <td>20</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",
       "      <td>20</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",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>5</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",
       "      <td>5</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",
       "      <td>5</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",
       "      <td>5</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",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2200 rows × 9 columns</p>\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   \n",
       "...   ...  ..  ..          ...        ...       ...         ...     ...   \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   \n",
       "\n",
       "      Label_encoded_data  \n",
       "0                     20  \n",
       "1                     20  \n",
       "2                     20  \n",
       "3                     20  \n",
       "4                     20  \n",
       "...                  ...  \n",
       "2195                   5  \n",
       "2196                   5  \n",
       "2197                   5  \n",
       "2198                   5  \n",
       "2199                   5  \n",
       "\n",
       "[2200 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32060ee5-b20a-469b-a231-b3da3fef7358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.31818181818181\n",
      "Training Accuracy: 1.0000\n",
      "Test Accuracy: 0.9932\n",
      "The model might be overfitting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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6tcT1ALgELtpFAYCBCtua68Ybbyyw/xdffGF16NDBqlKlilW3bl3rmWeesT7++ON82ykVtjXXyy+/nO+Ykpy2OCpsa67hw4fnGxsUFGTFxMQ4tSUnJ1utW7e2PDw8rJCQEOudd96xnn76acvT07OQTyG/cePGWZKshg0b5ntt27ZtVlRUlHXddddZdrvdql27tnX33XdbW7ZsyVfbXz+Dkrh4ay7LsqyzZ89aL774onXjjTdadrvd8vX1tdq2bWtNnjzZysjIcLznXr16WXXr1rU8PDysunXrWlFRUdaPP/7odKwPPvjAuuGGGxxblRW2TVfPnj0tT09PKzs7u9BaBw0aZFWuXNk6fvy4ZVmW9dtvv1kjRoyw6tWrZ3l4eFjXXnutFRMT43jdsv7cMmvcuHFW/fr1rcqVK1sBAQFWv379rH379jn6HDt2zOrbt69VtWpVy9fX13rkkUesnTt3Frg1V7Vq1Qqs7fvvv7fCw8MtLy8vy8/Pzxo6dKhjK7eL3/POnTutPn36WDVq1LA8PT2txo0bWxMmTMh3zJycHMvX19fy8fGxzpw5U+jnAuDy2SzrCpomAYArQO/evbVr1y799NNPri4Fhjp//rzq1q2rnj17at68ea4uB7iqsWYWwN/axT9Z+tNPP2n16tW69dZbXVMQrgorVqzQsWPHnG4qA1A+mJkF8LdWp04dDRo0SA0aNNAvv/yit956Szk5Odq+fbsaNWrk6vJgmK+//lrffvutpk6dKj8/P23bts3VJQFXPW4AA/C3duedd2rx4sVKS0uT3W5XWFiYpk2bRpDFJXnrrbf03nvvqVWrVlqwYIGrywH+Flw6M7tx40a9/PLL2rp1q44cOaLly5erd+/eRY7ZsGGDYmNjtWvXLgUGBmr8+PEaNGhQhdQLAACAK4tL18xmZ2erZcuWBW6yXZD9+/erR48euu2225SSkqInn3xSDz/8sD7++ONyrhQAAABXoitmzazNZit2Znb06NFatWqV0y+wDBgwQCdPntSaNWsqoEoAAABcSYxaM7tp0yaFh4c7tUVEROjJJ58sdExOTo7Tr+zk5eXpxIkTqlmzZql/rhEAAADlz7IsZWVlqW7dunJzK3ohgVFhNi0tTf7+/k5t/v7+yszM1JkzZ1SlSpV8Y+Li4jR58uSKKhEAAABl5ODBg7r22muL7GNUmL0UY8eOVWxsrON5RkaGrrvuOh08eFDe3t4VUoOPT4WcBoCLZWS4ugIAuDpkZmYqMDBQ1atXL7avUWE2ICBA6enpTm3p6eny9vYucFZWkux2u+x2e752b2/vCguzAP4euKQAQNkqyZJQo8JsWFiYVq9e7dS2du1ahYWFuagiAIAkaRH3IABXvYFXxJ4B+bh0a65Tp04pJSVFKSkpkv7ceislJUWpqamS/lwi8NefAvzHP/6hn3/+Wc8884z27NmjN998U++//76eeuopV5QPAAAAF3NpmN2yZYtat26t1q1bS5JiY2PVunVrTZw4UZJ05MgRR7CVpPr162vVqlVau3atWrZsqenTp+udd95RRESES+oHAACAa10x+8xWlMzMTPn4+CgjI6PC1syyAxjw9/D3uppehGUGwNWvApcZlCavuXRmFgAAALgchFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFguD7OzZ89WcHCwPD09FRoaqs2bNxfZf+bMmWrcuLGqVKmiwMBAPfXUU/rjjz8qqFoAAABcSVwaZpOSkhQbG6tJkyZp27ZtatmypSIiInT06NEC+y9atEhjxozRpEmTtHv3bs2bN09JSUl69tlnK7hyAAAAXAlcGmZnzJihoUOHavDgwbrhhhs0Z84cVa1aVfPnzy+w/5dffqlOnTpp4MCBCg4O1h133KGoqKhiZ3MBAABwdXJZmD179qy2bt2q8PDw/xXj5qbw8HBt2rSpwDEdO3bU1q1bHeH1559/1urVq9W9e/cKqRkAAABXlkquOvHx48eVm5srf39/p3Z/f3/t2bOnwDEDBw7U8ePHdfPNN8uyLJ0/f17/+Mc/ilxmkJOTo5ycHMfzzMzMsnkDAAAAcDmX3wBWGhs2bNC0adP05ptvatu2bVq2bJlWrVqlqVOnFjomLi5OPj4+jkdgYGAFVgwAAIDy5LKZWT8/P7m7uys9Pd2pPT09XQEBAQWOmTBhgh588EE9/PDDkqTmzZsrOztbw4YN07hx4+Tmlj+bjx07VrGxsY7nmZmZBFoAAICrhMtmZj08PNS2bVslJyc72vLy8pScnKywsLACx5w+fTpfYHV3d5ckWZZV4Bi73S5vb2+nBwAAAK4OLpuZlaTY2FjFxMSoXbt2uummmzRz5kxlZ2dr8ODBkqTo6GjVq1dPcXFxkqSePXtqxowZat26tUJDQ7V3715NmDBBPXv2dIRaAAAA/H24NMxGRkbq2LFjmjhxotLS0tSqVSutWbPGcVNYamqq00zs+PHjZbPZNH78eB0+fFi1atVSz5499fzzz7vqLQAAAMCFbFZh389fpTIzM+Xj46OMjIwKW3Jgs1XIaQC42N/ranqRRVzogKvewIq7yJUmrxm1mwEAAADwV4RZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYy+Vhdvbs2QoODpanp6dCQ0O1efPmIvufPHlSw4cPV506dWS323X99ddr9erVFVQtAAAAriSVXHnypKQkxcbGas6cOQoNDdXMmTMVERGhH374QbVr187X/+zZs7r99ttVu3ZtLV26VPXq1dMvv/yiGjVqVHzxAAAAcDmXhtkZM2Zo6NChGjx4sCRpzpw5WrVqlebPn68xY8bk6z9//nydOHFCX375pSpXrixJCg4OrsiSAQAAcAVx2TKDs2fPauvWrQoPD/9fMW5uCg8P16ZNmwocs3LlSoWFhWn48OHy9/dXs2bNNG3aNOXm5hZ6npycHGVmZjo9AAAAcHVwWZg9fvy4cnNz5e/v79Tu7++vtLS0Asf8/PPPWrp0qXJzc7V69WpNmDBB06dP1z//+c9CzxMXFycfHx/HIzAwsEzfBwAAAFzH5TeAlUZeXp5q166tt99+W23btlVkZKTGjRunOXPmFDpm7NixysjIcDwOHjxYgRUDAACgPLlszayfn5/c3d2Vnp7u1J6enq6AgIACx9SpU0eVK1eWu7u7o61p06ZKS0vT2bNn5eHhkW+M3W6X3W4v2+IBAABwRXDZzKyHh4fatm2r5ORkR1teXp6Sk5MVFhZW4JhOnTpp7969ysvLc7T9+OOPqlOnToFBFgAAAFc3ly4ziI2N1dy5c7Vw4ULt3r1bjz76qLKzsx27G0RHR2vs2LGO/o8++qhOnDihkSNH6scff9SqVas0bdo0DR8+3FVvAQAAAC7k0q25IiMjdezYMU2cOFFpaWlq1aqV1qxZ47gpLDU1VW5u/8vbgYGB+vjjj/XUU0+pRYsWqlevnkaOHKnRo0e76i0AAADAhWyWZVmuLqIiZWZmysfHRxkZGfL29q6Qc9psFXIaAC7297qaXmQRFzrgqjew4i5ypclrRu1mAAAAAPwVYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYqdZgNDg7WlClTlJqaWh71AAAAACVW6jD75JNPatmyZWrQoIFuv/12JSYmKicnpzxqAwAAAIp0SWE2JSVFmzdvVtOmTfX444+rTp06GjFihLZt21YeNQIAAAAFuuQ1s23atNHrr7+uX3/9VZMmTdI777yj9u3bq1WrVpo/f74syyrLOgEAAIB8Kl3qwHPnzmn58uWKj4/X2rVr1aFDBz300EM6dOiQnn32WX366adatGhRWdYKAAAAOCl1mN22bZvi4+O1ePFiubm5KTo6Wq+++qqaNGni6NOnTx+1b9++TAsFAAAALlbqMNu+fXvdfvvteuutt9S7d29Vrlw5X5/69etrwIABZVIgAAAAUJhSh9mff/5ZQUFBRfapVq2a4uPjL7koAAAAoCRKfQPY0aNH9fXXX+dr//rrr7Vly5YyKQoAAAAoiVKH2eHDh+vgwYP52g8fPqzhw4eXSVEAAABASZQ6zH7//fdq06ZNvvbWrVvr+++/L5OiAAAAgJIodZi12+1KT0/P137kyBFVqnTJO30BAAAApVbqMHvHHXdo7NixysjIcLSdPHlSzz77rG6//fYyLQ4AAAAoSqmnUl955RXdcsstCgoKUuvWrSVJKSkp8vf317vvvlvmBQIAAACFKXWYrVevnr799lslJCRox44dqlKligYPHqyoqKgC95wFAAAAysslLXKtVq2ahg0bVta1AAAAAKVyyXdsff/990pNTdXZs2ed2u+5557LLgoAAAAoiUv6BbA+ffrou+++k81mk2VZkiSbzSZJys3NLdsKAQAAgEKUejeDkSNHqn79+jp69KiqVq2qXbt2aePGjWrXrp02bNhQDiUCAAAABSv1zOymTZu0bt06+fn5yc3NTW5ubrr55psVFxenJ554Qtu3by+POgEAAIB8Sj0zm5ubq+rVq0uS/Pz89Ouvv0qSgoKC9MMPP5RtdQAAAEARSj0z26xZM+3YsUP169dXaGioXnrpJXl4eOjtt99WgwYNyqNGAAAAoEClDrPjx49Xdna2JGnKlCm6++671blzZ9WsWVNJSUllXiAAAABQmFKH2YiICMd/N2zYUHv27NGJEyfk6+vr2NEAAAAAqAilWjN77tw5VapUSTt37nRqv+aaawiyAAAAqHClCrOVK1fWddddx16yAAAAuCKUejeDcePG6dlnn9WJEyfKox4AAACgxEq9ZnbWrFnau3ev6tatq6CgIFWrVs3p9W3btpVZcQAAAEBRSh1me/fuXQ5lAAAAAKVX6jA7adKk8qgDAAAAKLVSr5kFAAAArhSlnpl1c3MrchsudjoAAABARSl1mF2+fLnT83Pnzmn79u1auHChJk+eXGaFAQAAAMUpdZjt1atXvrZ+/frpxhtvVFJSkh566KEyKQwAAAAoTpmtme3QoYOSk5PL6nAAAABAscokzJ45c0avv/666tWrVxaHAwAAAEqk1MsMfH19nW4AsyxLWVlZqlq1qt57770yLQ4AAAAoSqnD7KuvvuoUZt3c3FSrVi2FhobK19e3TIsDAAAAilLqMDto0KByKAMAAAAovVKvmY2Pj9eSJUvytS9ZskQLFy4sk6IAAACAkih1mI2Li5Ofn1++9tq1a2vatGllUhQAAABQEqUOs6mpqapfv36+9qCgIKWmppZJUQAAAEBJlDrM1q5dW99++22+9h07dqhmzZplUhQAAABQEqUOs1FRUXriiSe0fv165ebmKjc3V+vWrdPIkSM1YMCA8qgRAAAAKFCpdzOYOnWqDhw4oG7duqlSpT+H5+XlKTo6mjWzAAAAqFA2y7KsSxn4008/KSUlRVWqVFHz5s0VFBRU1rWVi8zMTPn4+CgjI0Pe3t4Vcs6/bMsL4Cp2aVfTq8QiLnTAVW9gxV3kSpPXSj0ze0GjRo3UqFGjSx0OAAAAXLZSr5nt27evXnzxxXztL730ku67774yKQoAAAAoiVKH2Y0bN6p79+752u+66y5t3LixTIoCAAAASqLUYfbUqVPy8PDI1165cmVlZmaWSVEAAABASZQ6zDZv3lxJSUn52hMTE3XDDTeUSVEAAABASZT6BrAJEybo3nvv1b59+9S1a1dJUnJyshYtWqSlS5eWeYEAAABAYUodZnv27KkVK1Zo2rRpWrp0qapUqaKWLVtq3bp1uuaaa8qjRgAAAKBAl7Q1V48ePdSjRw9Jf+4DtnjxYo0aNUpbt25Vbm5umRYIAAAAFKbUa2Yv2Lhxo2JiYlS3bl1Nnz5dXbt21VdffVWWtQEAAABFKtXMbFpamhYsWKB58+YpMzNT/fv3V05OjlasWMHNXwAAAKhwJZ6Z7dmzpxo3bqxvv/1WM2fO1K+//qo33nijPGsDAAAAilTimdmPPvpITzzxhB599FF+xhYAAABXhBLPzH7++efKyspS27ZtFRoaqlmzZun48ePlWRsAAABQpBKH2Q4dOmju3Lk6cuSIHnnkESUmJqpu3brKy8vT2rVrlZWVVZ51AgAAAPmUejeDatWqaciQIfr888/13Xff6emnn9YLL7yg2rVr65577imPGgEAAIACXfLWXJLUuHFjvfTSSzp06JAWL15cVjUBAAAAJXJZYfYCd3d39e7dWytXriyLwwEAAAAlUiZhFgAAAHAFwiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAY10RYXb27NkKDg6Wp6enQkNDtXnz5hKNS0xMlM1mU+/evcu3QAAAAFyRXB5mk5KSFBsbq0mTJmnbtm1q2bKlIiIidPTo0SLHHThwQKNGjVLnzp0rqFIAAABcaVweZmfMmKGhQ4dq8ODBuuGGGzRnzhxVrVpV8+fPL3RMbm6u7r//fk2ePFkNGjSowGoBAABwJXFpmD179qy2bt2q8PBwR5ubm5vCw8O1adOmQsdNmTJFtWvX1kMPPVTsOXJycpSZmen0AAAAwNXBpWH2+PHjys3Nlb+/v1O7v7+/0tLSChzz+eefa968eZo7d26JzhEXFycfHx/HIzAw8LLrBgAAwJXB5csMSiMrK0sPPvig5s6dKz8/vxKNGTt2rDIyMhyPgwcPlnOVAAAAqCiVXHlyPz8/ubu7Kz093ak9PT1dAQEB+frv27dPBw4cUM+ePR1teXl5kqRKlSrphx9+UEhIiNMYu90uu91eDtUDAADA1Vw6M+vh4aG2bdsqOTnZ0ZaXl6fk5GSFhYXl69+kSRN99913SklJcTzuuece3XbbbUpJSWEJAQAAwN+MS2dmJSk2NlYxMTFq166dbrrpJs2cOVPZ2dkaPHiwJCk6Olr16tVTXFycPD091axZM6fxNWrUkKR87QAAALj6uTzMRkZG6tixY5o4caLS0tLUqlUrrVmzxnFTWGpqqtzcjFraCwAAgApisyzLcnURFSkzM1M+Pj7KyMiQt7d3hZzTZquQ0wBwsb/X1fQii7jQAVe9gRV3kStNXmPKEwAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxroiwuzs2bMVHBwsT09PhYaGavPmzYX2nTt3rjp37ixfX1/5+voqPDy8yP4AAAC4erk8zCYlJSk2NlaTJk3Stm3b1LJlS0VEROjo0aMF9t+wYYOioqK0fv16bdq0SYGBgbrjjjt0+PDhCq4cAAAArmazLMtyZQGhoaFq3769Zs2aJUnKy8tTYGCgHn/8cY0ZM6bY8bm5ufL19dWsWbMUHR1dbP/MzEz5+PgoIyND3t7el11/SdhsFXIaAC7m2qupiy3iQgdc9QZW3EWuNHnNpTOzZ8+e1datWxUeHu5oc3NzU3h4uDZt2lSiY5w+fVrnzp3TNddcU15lAgAA4ApVyZUnP378uHJzc+Xv7+/U7u/vrz179pToGKNHj1bdunWdAvFf5eTkKCcnx/E8MzPz0gsGAADAFcXla2YvxwsvvKDExEQtX75cnp6eBfaJi4uTj4+P4xEYGFjBVQIAAKC8uDTM+vn5yd3dXenp6U7t6enpCggIKHLsK6+8ohdeeEGffPKJWrRoUWi/sWPHKiMjw/E4ePBgmdQOAAAA13NpmPXw8FDbtm2VnJzsaMvLy1NycrLCwsIKHffSSy9p6tSpWrNmjdq1a1fkOex2u7y9vZ0eAAAAuDq4dM2sJMXGxiomJkbt2rXTTTfdpJkzZyo7O1uDBw+WJEVHR6tevXqKi4uTJL344ouaOHGiFi1apODgYKWlpUmSvLy85OXl5bL3AQAAgIrn8jAbGRmpY8eOaeLEiUpLS1OrVq20Zs0ax01hqampcnP73wTyW2+9pbNnz6pfv35Ox5k0aZKee+65iiwdAAAALubyfWYrGvvMAigvf6+r6UXYZxa4+rHPLAAAAFC2CLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAABjEWYBAABgLMIsAAAAjEWYBQAAgLEIswAAADDWFRFmZ8+ereDgYHl6eio0NFSbN28usv+SJUvUpEkTeXp6qnnz5lq9enUFVQoAAIAricvDbFJSkmJjYzVp0iRt27ZNLVu2VEREhI4ePVpg/y+//FJRUVF66KGHtH37dvXu3Vu9e/fWzp07K7hyAAAAuJrNsizLlQWEhoaqffv2mjVrliQpLy9PgYGBevzxxzVmzJh8/SMjI5Wdna3//ve/jrYOHTqoVatWmjNnTrHny8zMlI+PjzIyMuTt7V12b6QINluFnAaAi7n2aupii7jQAVe9gRV3kStNXqtUQTUV6OzZs9q6davGjh3raHNzc1N4eLg2bdpU4JhNmzYpNjbWqS0iIkIrVqwosH9OTo5ycnIczzMyMiT9+SEBQFn6W19WTru6AADlrgIvchdyWknmXF0aZo8fP67c3Fz5+/s7tfv7+2vPnj0FjklLSyuwf1paWoH94+LiNHny5HztgYGBl1g1ABTMx8fVFQBAORpa8Re5rKws+RRzcXVpmK0IY8eOdZrJzcvL04kTJ1SzZk3Z+P4f5SQzM1OBgYE6ePBghS1nAYCKwjUO5c2yLGVlZalu3brF9nVpmPXz85O7u7vS09Od2tPT0xUQEFDgmICAgFL1t9vtstvtTm01atS49KKBUvD29uZCD+CqxTUO5am4GdkLXLqbgYeHh9q2bavk5GRHW15enpKTkxUWFlbgmLCwMKf+krR27dpC+wMAAODq5fJlBrGxsYqJiVG7du100003aebMmcrOztbgwYMlSdHR0apXr57i4uIkSSNHjlSXLl00ffp09ejRQ4mJidqyZYvefvttV74NAAAAuIDLw2xkZKSOHTumiRMnKi0tTa1atdKaNWscN3mlpqbKze1/E8gdO3bUokWLNH78eD377LNq1KiRVqxYoWbNmrnqLQD52O12TZo0Kd8SFwC4GnCNw5XE5fvMAgAAAJfK5b8ABgAAAFwqwiwAAACMRZgFAACAsQizMFZwcLBmzpxZ4v4bNmyQzWbTyZMny60mAABQsQizKHc2m63Ix3PPPXdJx/3mm280bNiwEvfv2LGjjhw5UuJNmMtCkyZNZLfbC/25ZQB/X+V1bbxw7BUrVpS4/yOPPCJ3d3ctWbLkks8JuAphFuXuyJEjjsfMmTPl7e3t1DZq1ChHX8uydP78+RIdt1atWqpatWqJ6/Dw8FBAQECF/Yzx559/rjNnzqhfv35auHBhhZyzKOfOnXN1CQD+ojTXxvJ0+vRpJSYm6plnntH8+fMr5JxFOXv2rKtLgGEIsyh3AQEBjoePj49sNpvj+Z49e1S9enV99NFHatu2rex2uz7//HPt27dPvXr1kr+/v7y8vNS+fXt9+umnTse9eJmBzWbTO++8oz59+qhq1apq1KiRVq5c6Xj94mUGCxYsUI0aNfTxxx+radOm8vLy0p133qkjR444xpw/f15PPPGEatSooZo1a2r06NGKiYlR7969i33f8+bN08CBA/Xggw8W+BfEoUOHFBUVpWuuuUbVqlVTu3bt9PXXXzte//DDD9W+fXt5enrKz89Pffr0cXqvF8+61KhRQwsWLJAkHThwQDabTUlJSerSpYs8PT2VkJCg3377TVFRUapXr56qVq2q5s2ba/HixU7HycvL00svvaSGDRvKbrfruuuu0/PPPy9J6tq1q0aMGOHU/9ixY/Lw8Mj3y3wAilbUtTEgIECJiYlq2rSpPD091aRJE7355puOsWfPntWIESNUp04deXp6KigoyPHjQsHBwZKkPn36yGazOZ4XZsmSJbrhhhs0ZswYbdy4UQcPHnR6PScnR6NHj1ZgYKDsdrsaNmyoefPmOV7ftWuX7r77bnl7e6t69erq3Lmz9u3bJ0m69dZb9eSTTzodr3fv3ho0aJDjeXBwsKZOnaro6Gh5e3s7vnEbPXq0rr/+elWtWlUNGjTQhAkT8v2jvLDr5JQpUwrcf75Vq1aaMGFCkZ8HzEOYxRVhzJgxeuGFF7R79261aNFCp06dUvfu3ZWcnKzt27frzjvvVM+ePZWamlrkcSZPnqz+/fvr22+/Vffu3XX//ffrxIkThfY/ffq0XnnlFb377rvauHGjUlNTnWZDXnzxRSUkJCg+Pl5ffPGFMjMzS/TVXVZWlpYsWaIHHnhAt99+uzIyMvTZZ585Xj916pS6dOmiw4cPa+XKldqxY4eeeeYZ5eXlSZJWrVqlPn36qHv37tq+fbuSk5N10003FXvei40ZM0YjR47U7t27FRERoT/++ENt27bVqlWrtHPnTg0bNkwPPvigNm/e7BgzduxYvfDCC5owYYK+//57LVq0yPEjJg8//LAWLVqknJwcR//33ntP9erVU9euXUtdH4CCJSQkaOLEiXr++ee1e/duTZs2TRMmTHB8y/P6669r5cqVev/99/XDDz8oISHBEVq/+eYbSVJ8fLyOHDnieF6YefPm6YEHHpCPj4/uuusuxz+KL4iOjtbixYv1+uuva/fu3frXv/4lLy8vSdLhw4d1yy23yG63a926ddq6dauGDBlS4m/YLnjllVfUsmVLbd++3RE2q1evrgULFuj777/Xa6+9prlz5+rVV191jCnqOjlkyBDt3r3b6b1v375d3377reMXRnEVsYAKFB8fb/n4+Dier1+/3pJkrVixotixN954o/XGG284ngcFBVmvvvqq47kka/z48Y7np06dsiRZH330kdO5fv/9d0ctkqy9e/c6xsyePdvy9/d3PPf397defvllx/Pz589b1113ndWrV68ia3377betVq1aOZ6PHDnSiomJcTz/17/+ZVWvXt367bffChwfFhZm3X///YUeX5K1fPlypzYfHx8rPj7esizL2r9/vyXJmjlzZpF1WpZl9ejRw3r66acty7KszMxMy263W3Pnzi2w75kzZyxfX18rKSnJ0daiRQvrueeeK/Y8AAp38bUxJCTEWrRokVOfqVOnWmFhYZZlWdbjjz9ude3a1crLyyvweAVdIwry448/WpUrV7aOHTtmWZZlLV++3Kpfv77juD/88IMlyVq7dm2B48eOHWvVr1/fOnv2bIGvd+nSxRo5cqRTW69evZyuh0FBQVbv3r2LrfXll1+22rZt63he3HXyrrvush599FHH88cff9y69dZbiz0PzMPMLK4I7dq1c3p+6tQpjRo1Sk2bNlWNGjXk5eWl3bt3Fzsz26JFC8d/V6tWTd7e3jp69Gih/atWraqQkBDH8zp16jj6Z2RkKD093WlG1N3dXW3bti32/cyfP18PPPCA4/kDDzygJUuWKCsrS5KUkpKi1q1b65prrilwfEpKirp161bseYpz8eeam5urqVOnqnnz5rrmmmvk5eWljz/+2PG57t69Wzk5OYWe29PT02nZxLZt27Rz506nrwwBXJ7s7Gzt27dPDz30kLy8vByPf/7zn46v7wcNGqSUlBQ1btxYTzzxhD755JNLOtf8+fMVEREhPz8/SVL37t2VkZGhdevWSfrzWuTu7q4uXboUOD4lJUWdO3dW5cqVL+n8F1x8rZKkpKQkderUSQEBAfLy8tL48eOd/g4o7jo5dOhQLV68WH/88YfOnj2rRYsWaciQIZdVJ65MlVxdACD9GTz/atSoUVq7dq1eeeUVNWzYUFWqVFG/fv2KvTHg4guqzWZzfHVf0v7WZf7C8/fff6+vvvpKmzdv1ujRox3tubm5SkxM1NChQ1WlSpUij1Hc6wXVWdANXhd/ri+//LJee+01zZw5U82bN1e1atX05JNPOj7X4s4r/bnUoFWrVjp06JDi4+PVtWtXBQUFFTsOQMmcOnVKkjR37lyFhoY6vebu7i5JatOmjfbv36+PPvpIn376qfr376/w8HAtXbq0xOfJzc3VwoULlZaWpkqVKjm1z58/X926dbvsa5Wbm9slXas2bdqk+++/X5MnT1ZERIR8fHyUmJio6dOnl/jcPXv2lN1u1/Lly+Xh4aFz586pX79+RY6BmZiZxRXpiy++0KBBg9SnTx81b95cAQEBOnDgQIXW4OPjI39/f6c1V7m5udq2bVuR4+bNm6dbbrlFO3bsUEpKiuMRGxvruGmiRYsWSklJKXQ9b4sWLYq8oapWrVpON6r99NNPOn36dLHv6YsvvlCvXr30wAMPqGXLlmrQoIF+/PFHx+uNGjVSlSpVijx38+bN1a5dO82dO5eZDqAc+Pv7q27duvr555/VsGFDp0f9+vUd/by9vRUZGam5c+cqKSlJ//nPfxzXlMqVKys3N7fI86xevVpZWVnavn2707Vq8eLFWrZsmU6ePKnmzZsrLy9P//d//1fgMVq0aKHPPvus0N1SLr5W5ebmaufOncV+Bl9++aWCgoI0btw4tWvXTo0aNdIvv/yS79xFXasqVaqkmJgYxcfHKz4+XgMGDCjRP9hhHmZmcUVq1KiRli1bpp49e8pms2nChAlFzrCWl8cff1xxcXFq2LChmjRpojfeeEO///57odt7nTt3Tu+++26Bd9I+/PDDmjFjhnbt2qWoqChNmzZNvXv3VlxcnOrUqaPt27erbt26CgsL06RJk9StWzeFhIRowIABOn/+vFavXu2Y6e3atatmzZqlsLAw5ebmavTo0SX6mq9Ro0ZaunSpvvzyS/n6+mrGjBlKT0/XDTfcIOnPZQSjR4/WM888Iw8PD3Xq1EnHjh3Trl279NBDDzm9lxEjRqhatWpOuywAKBuTJ0/WE088IR8fH915553KycnRli1b9Pvvvys2NlYzZsxQnTp11Lp1a7m5uWnJkiUKCAhQjRo1JP25Q0BycrI6deoku90uX1/ffOeYN2+eevTooZYtWzq133DDDXrqqaeUkJCg4cOHKyYmRkOGDNHrr7+uli1b6pdfftHRo0fVv39/jRgxQm+88YYGDBigsWPHysfHR1999ZVuuukmNW7cWF27dlVsbKxWrVqlkJAQzZgxo0Q/XNOoUSOlpqYqMTFR7du316pVq7R8+XKnPsVdJ6U/r1VNmzaV9Oc/5nF1YmYWV6QZM2bI19dXHTt2VM+ePRUREaE2bdpUeB2jR49WVFSUoqOjFRYWJi8vL0VERMjT07PA/itXrtRvv/1WYMBr2rSpmjZtqnnz5snDw0OffPKJateure7du6t58+Z64YUXHF8h3nrrrVqyZIlWrlypVq1aqWvXrk47DkyfPl2BgYHq3LmzBg4cqFGjRpVoz93x48erTZs2ioiI0K233qqAgIB824xNmDBBTz/9tCZOnKimTZsqMjIy37rjqKgoVapUSVFRUYV+FgAu3cMPP6x33nlH8fHxat68ubp06aIFCxY4ZmarV6+ul156Se3atVP79u114MABrV69Wm5uf/61Pn36dK1du1aBgYFq3bp1vuOnp6dr1apV6tu3b77X3Nzc1KdPH8c3SW+99Zb69eunxx57TE2aNNHQoUOVnZ0tSapZs6bWrVvn2KGlbdu2mjt3ruMf10OGDFFMTIyio6PVpUsXNWjQQLfddlux7/+ee+7RU089pREjRqhVq1b68ssv822pVdx1UvozFHfs2FFNmjTJt2QDVw+bdbkLBIG/kby8PDVt2lT9+/fX1KlTXV2Oyxw4cEAhISH65ptvXPKPDAAoCcuy1KhRIz322GOKjY11dTkoJywzAIrwyy+/6JNPPlGXLl2Uk5OjWbNmaf/+/Ro4cKCrS3OJc+fO6bffftP48ePVoUMHgiyAK9axY8eUmJiotLQ09pa9yhFmgSK4ublpwYIFGjVqlCzLUrNmzfTpp5861mD93XzxxRe67bbbdP3115fqrmkAqGi1a9eWn5+f3n777QLXDOPqwTIDAAAAGIsbwAAAAGAswiwAAACMRZgFAACAsQizAAAAMBZhFgAAAMYizAIAAMBYhFkAAAAYizALAAAAYxFmAQAAYKz/D7gCjlUUVItxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pNQxJp6DDLdd27rnnZtv/ww8/DBo3bhwULlw4KF++fPDggw8G77zzTpYltg63XNuTTz6Z5ZhApmWvDrdc2913351lbKVKlYKuXbtmaps7d25w3nnnBVFRUUFSUlLwj3/8I/jrX/8axMTEHOZZyOrhhx8OgKBq1apZ9i1dujTo3LlzULFixSA6OjooU6ZMcM011wSfffZZltp+/xzkxKHLtQVBEOzbty/429/+Fpx77rlBdHR0ULJkyaB+/frBwIEDg5SUlNA1X3fddUH58uWDqKiooHz58kHnzp2Db775JtOx3nzzzeCcc84JLV93uKXb2rRpE8TExAS7d+8+bK3dunULChUqFGzbti0IgiD4+eefg549ewYVKlQIoqKigjPPPDPo2rVraH8Q/LaM2sMPPxxUqVIlKFSoUFC2bNmgffv2wZo1a0J9tm7dGtxwww1BkSJFgpIlSwZ33nlnsGLFimyXaytatGi2tX355ZdBy5Ytg2LFigXx8fHB7bffHlre79BrXrFiRdCuXbugRIkSQUxMTFC9evWgX79+WY6ZlpYWlCxZMoiLiwv27t172OdF0sklIghOots3knQSaNu2LStXruTbb78Ndyk6RR04cIDy5cvTpk0bRo8eHe5yJOWQc4wl/aEd+rW/3377LbNmzeKSSy4JT0E6LUyfPp2tW7dm+kCfpJOfd4wl/aGVK1eObt26cdZZZ/H999/z0ksvkZaWxrJly6hWrVq4y9Mp5pNPPuGLL75g8ODBxMfHs3Tp0nCXJCkX/PCdpD+0K6+8kgkTJrBp0yaio6Np0qQJQ4YMMRTrmLz00ku8/vrr1KtXj7Fjx4a7HEm5FNY7xgsXLuTJJ59kyZIlbNy4kWnTptG2bdsjjlmwYAHJycmsXLmSxMREHnnkEbp165Yv9UqSJOn0FdY5xrt376Zu3brZLgifnbVr13L11Vdz6aWXsnz5cu677z5uu+023nnnnRNcqSRJkk53J80c44iIiKPeMe7duzczZ87M9M1DnTp1YseOHcyePTsfqpQkSdLp6pSaY7xo0SJatmyZqa1Vq1bcd999hx2TlpaW6dulMjIy2L59O6VKlcr1V55KkiTpxAuCgJ07d1K+fHkiI/NvgsMpFYw3bdpEQkJCpraEhARSU1PZu3cvhQsXzjJm6NChDBw4ML9KlCRJUh754YcfOPPMM/PtfKdUMD4Wffv2JTk5ObSdkpJCxYoV+eGHH4iNjc3z833//veMv2b8Ufs1fbAppaqVIgiC374iN4Ag47efg4z/bv/+5//uI+O38dntO9K40DlyOC5L38PUkqmdXNaU8d9ZPP9ty9NrycjcL7tzHHEcRzlmNnVK+p+IiAiI+O3/IyL/+3NkRObtQ38+3L5sxh38hMwRj5ndvogc1PLffaFz5PQaDu47zLVnOvahxwKIzOY5yaNrCZ0jm1qO5VqONO7g7+WE/v4jyPk1HNwmd7/H0Ln+oDLSM3ip1kuk/pR62D6xFWK56z93EVkg7+/opqamkpiYSPHixfP82EdySgXjsmXLsnnz5kxtmzdvJjY2Ntu7xQDR0dFER0dnaY+NjT0hwfjcK8+lzJllSN2Q+lvAPFQExJ4ZS+shrU/IH5LCK9cvWHIT1k+ifTm6plNl38lYU5j/VvLmPwb+929gek66//Y/6aSTXZDO7QuyU3Df7i272ffTPmKIOexTs2/DPnZ8voPKl1Q+cU9/Pr84OaWCcZMmTZg1a1amtjlz5tCkSZMwVZRVZIFIrnz2St5o/8Zvr2h//+/8f3+3Vw6/0lB8moqIiCCiwB/3DoNOD9m+K3MSB/k/9Iu+Q1/UnAw1nUR/D3nzHwQE6b5wO5ydG3eGu4Q8FdZgvGvXLlavXh3aXrt2LcuXL+eMM86gYsWK9O3blw0bNvDPf/4TgD//+c+MGDGCBx98kB49ejBv3jzeeOMNZs6cGa5LyFbN62vSYUoHZveaTeqP/3sLIvbMWK4cfiU1r68Zxuok6chCbyMTAQXCXY107HI7jTDcQf5kemH38zc/89lLnx31OS5eLn+nOpxoYQ3Gn332GZdeemlo++Bc4K5duzJ27Fg2btzI+vXrQ/urVKnCzJkzuf/++3n22Wc588wz+cc//kGrVq3yvfajqXl9TapfV531769n58adFC9XnIrNKnqnWJKkfOKLvGOXkZ7BN299c9SpoRWbVcz32k6kk2Yd4/ySmppKXFwcKSkpJ2SOsSRJ0ulg1dRVv00Nhczh+L8zBjtM6XDC3gUPV17z9qUkSZKyODg1NLZC5mAae2bsCQ3F4XRKffhOkiRJ+eePNjXUYCxJkqTDiiwQeUKXZDuZnJ5xX5IkScolg7EkSZKEwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBJwEwfiFF16gcuXKxMTE0KhRIxYvXnzE/sOHD6d69eoULlyYxMRE7r//fn799dd8qlaSJEmnq7AG40mTJpGcnMyAAQNYunQpdevWpVWrVmzZsiXb/uPHj6dPnz4MGDCAVatWMXr0aCZNmsRDDz2Uz5VLkiTpdBPWYDxs2DBuv/12unfvzjnnnMPIkSMpUqQIr7zySrb9P/roI5o2bcpNN91E5cqVueKKK+jcufNR7zJLkiRJRxO2YLxv3z6WLFlCy5Yt/1dMZCQtW7Zk0aJF2Y658MILWbJkSSgIf/fdd8yaNYvWrVvnS82SJEk6fRUM14m3bdtGeno6CQkJmdoTEhL46quvsh1z0003sW3bNi666CKCIODAgQP8+c9/PuJUirS0NNLS0kLbqampeXMBkiRJOq2E/cN3ubFgwQKGDBnCiy++yNKlS5k6dSozZ85k8ODBhx0zdOhQ4uLiQo/ExMR8rFiSJEmnioggCIJwnHjfvn0UKVKEKVOm0LZt21B7165d2bFjB2+++WaWMc2aNaNx48Y8+eSTobbXX3+dO+64g127dhEZmTXnZ3fHODExkZSUFGJjY/P2oiRJknTcUlNTiYuLy/e8FrY7xlFRUdSvX5+5c+eG2jIyMpg7dy5NmjTJdsyePXuyhN8CBQoAcLh8Hx0dTWxsbKaHJEmSdKiwzTEGSE5OpmvXrjRo0IALLriA4cOHs3v3brp37w5Aly5dqFChAkOHDgWgTZs2DBs2jPPOO49GjRqxevVq+vXrR5s2bUIBWZIkSToWYQ3GHTt2ZOvWrfTv359NmzZRr149Zs+eHfpA3vr16zPdIX7kkUeIiIjgkUceYcOGDZQuXZo2bdrw2GOPhesSJEmSdJoI2xzjcAnXnBVJkiTlzB9ujrEkSZJ0MjEYS5IkSRiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBJwEwfiFF16gcuXKxMTE0KhRIxYvXnzE/jt27ODuu++mXLlyREdHc/bZZzNr1qx8qlaSJEmnq4LhPPmkSZNITk5m5MiRNGrUiOHDh9OqVSu+/vprypQpk6X/vn37uPzyyylTpgxTpkyhQoUKfP/995QoUSL/i5ckSdJpJSIIgiBcJ2/UqBENGzZkxIgRAGRkZJCYmMg999xDnz59svQfOXIkTz75JF999RWFChU6pnOmpqYSFxdHSkoKsbGxx1W/JEmS8l648lrYplLs27ePJUuW0LJly/8VExlJy5YtWbRoUbZjZsyYQZMmTbj77rtJSEigVq1aDBkyhPT09MOeJy0tjdTU1EwPSZIk6VBhC8bbtm0jPT2dhISETO0JCQls2rQp2zHfffcdU6ZMIT09nVmzZtGvXz+efvpp/u///u+w5xk6dChxcXGhR2JiYp5ehyRJkk4PYf/wXW5kZGRQpkwZXn75ZerXr0/Hjh15+OGHGTly5GHH9O3bl5SUlNDjhx9+yMeKJUmSdKoI24fv4uPjKVCgAJs3b87UvnnzZsqWLZvtmHLlylGoUCEKFCgQaqtZsyabNm1i3759REVFZRkTHR1NdHR03hYvSZKk007Y7hhHRUVRv3595s6dG2rLyMhg7ty5NGnSJNsxTZs2ZfXq1WRkZITavvnmG8qVK5dtKJYkSZJyKqxTKZKTkxk1ahSvvvoqq1at4q677mL37t10794dgC5dutC3b99Q/7vuuovt27fTq1cvvvnmG2bOnMmQIUO4++67w3UJkiRJOk2EdR3jjh07snXrVvr378+mTZuoV68es2fPDn0gb/369URG/i+7JyYm8s4773D//fdTp04dKlSoQK9evejdu3e4LkGSJEmnibCuYxwOrmMsSZJ0cvvDrWMsSZIknUwMxpIkSRIGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAHHEIwrV67MoEGDWL9+/YmoR5IkSQqLXAfj++67j6lTp3LWWWdx+eWXM3HiRNLS0k5EbZIkSVK+OaZgvHz5chYvXkzNmjW55557KFeuHD179mTp0qUnokZJkiTphIsIgiA4ngPs37+fF198kd69e7N//35q167NvffeS/fu3YmIiMirOvNMamoqcXFxpKSkEBsbG+5yJEmSdIhw5bWCxzpw//79TJs2jTFjxjBnzhwaN27Mrbfeyo8//shDDz3Ee++9x/jx4/OyVkmSJOmEyXUwXrp0KWPGjGHChAlERkbSpUsXnnnmGWrUqBHq065dOxo2bJinhUqSJEknUq6DccOGDbn88st56aWXaNu2LYUKFcrSp0qVKnTq1ClPCpQkSZLyQ66D8XfffUelSpWO2Kdo0aKMGTPmmIuSJEmS8luuV6XYsmULn3zySZb2Tz75hM8++yxPipIkSZLyW66D8d13380PP/yQpX3Dhg3cfffdeVKUJEmSlN9yHYy//PJLzj///Czt5513Hl9++WWeFCVJkiTlt1wH4+joaDZv3pylfePGjRQseMyrv0mSJElhletgfMUVV9C3b19SUlJCbTt27OChhx7i8ssvz9PiJEmSpPyS61u8Tz31FBdffDGVKlXivPPOA2D58uUkJCTw2muv5XmBkiRJUn7IdTCuUKECX3zxBePGjePzzz+ncOHCdO/enc6dO2e7prEkSZJ0KjimScFFixbljjvuyOtaJEmSpLA55k/Lffnll6xfv559+/Zlar/22muPuyhJkiQpvx3TN9+1a9eO//znP0RERBAEAQAREREApKen522FkiRJUj7I9aoUvXr1okqVKmzZsoUiRYqwcuVKFi5cSIMGDViwYMEJKFGSJEk68XJ9x3jRokXMmzeP+Ph4IiMjiYyM5KKLLmLo0KHce++9LFu27ETUKUmSJJ1Qub5jnJ6eTvHixQGIj4/np59+AqBSpUp8/fXXeVudJEmSlE9yfce4Vq1afP7551SpUoVGjRrxxBNPEBUVxcsvv8xZZ511ImqUJEmSTrhcB+NHHnmE3bt3AzBo0CCuueYamjVrRqlSpZg0aVKeFyhJkiTlh4jg4LISx2H79u2ULFkytDLFySw1NZW4uDhSUlKIjY0NdzmSJEk6RLjyWq7mGO/fv5+CBQuyYsWKTO1nnHHGKRGKJUmSpMPJVTAuVKgQFStWdK1iSZIknXZyvSrFww8/zEMPPcT27dtPRD2SJElSWOT6w3cjRoxg9erVlC9fnkqVKlG0aNFM+5cuXZpnxUmSJEn5JdfBuG3btiegDEmSJCm88mRVilOJq1JIkiSd3E6JVSkkSZKk01Wup1JERkYecWk2V6yQJEnSqSjXwXjatGmZtvfv38+yZct49dVXGThwYJ4VJkmSJOWnPJtjPH78eCZNmsSbb76ZF4c7YZxjLEmSdHI75ecYN27cmLlz5+bV4SRJkqR8lSfBeO/evTz33HNUqFAhLw4nSZIk5btczzEuWbJkpg/fBUHAzp07KVKkCK+//nqeFidJkiTll1wH42eeeSZTMI6MjKR06dI0atSIkiVL5mlxkiRJUn7JdTDu1q3bCShDkiRJCq9czzEeM2YMkydPztI+efJkXn311TwpSpIkScpvuQ7GQ4cOJT4+Pkt7mTJlGDJkSJ4UJUmSJOW3XAfj9evXU6VKlSztlSpVYv369XlSlCRJkpTfch2My5QpwxdffJGl/fPPP6dUqVJ5UpQkSZKU33IdjDt37sy9997L/PnzSU9PJz09nXnz5tGrVy86dep0ImqUJEmSTrhcr0oxePBg1q1bx2WXXUbBgr8Nz8jIoEuXLs4xliRJ0ikrIgiC4FgGfvvttyxfvpzChQtTu3ZtKlWqlNe1nRDh+u5tSZIk5Uy48lqu7xgfVK1aNapVq5aXtUiSJElhk+s5xjfccAN/+9vfsrQ/8cQT3HjjjXlSlCRJkpTfch2MFy5cSOvWrbO0X3XVVSxcuDBPipIkSZLyW66D8a5du4iKisrSXqhQIVJTU/OkKEmSJCm/5ToY165dm0mTJmVpnzhxIuecc06eFCVJkiTlt1x/+K5fv35cf/31rFmzhhYtWgAwd+5cxo8fz5QpU/K8QEmSJCk/5DoYt2nThunTpzNkyBCmTJlC4cKFqVu3LvPmzeOMM844ETVKkiRJJ9wxr2N8UGpqKhMmTGD06NEsWbKE9PT0vKrthHAdY0mSpJNbuPJarucYH7Rw4UK6du1K+fLlefrpp2nRogUff/xxXtYmSZIk5ZtcTaXYtGkTY8eOZfTo0aSmptKhQwfS0tKYPn26H7yTJEnSKS3Hd4zbtGlD9erV+eKLLxg+fDg//fQTzz///ImsTZIkSco3Ob5j/Pbbb3Pvvfdy1113+VXQkiRJOu3k+I7xBx98wM6dO6lfvz6NGjVixIgRbNu27UTWJkmSJOWbHAfjxo0bM2rUKDZu3Midd97JxIkTKV++PBkZGcyZM4edO3eeyDolSZKkE+q4lmv7+uuvGT16NK+99ho7duzg8ssvZ8aMGXlZX55zuTZJkqST2ym3XBtA9erVeeKJJ/jxxx+ZMGFCXtUkSZIk5bvj/oKPU413jCVJkk5up+QdY0mSJOl0YTCWJEmSMBhLkiRJgMFYkiRJAgzGkiRJEmAwliRJkgCDsSRJkgQYjCVJkiTgJAnGL7zwApUrVyYmJoZGjRqxePHiHI2bOHEiERERtG3b9sQWKEmSpNNe2IPxpEmTSE5OZsCAASxdupS6devSqlUrtmzZcsRx69at44EHHqBZs2b5VKkkSZJOZ2EPxsOGDeP222+ne/funHPOOYwcOZIiRYrwyiuvHHZMeno6N998MwMHDuSss87Kx2olSZJ0ugprMN63bx9LliyhZcuWobbIyEhatmzJokWLDjtu0KBBlClThltvvfWo50hLSyM1NTXTQ5IkSTpUWIPxtm3bSE9PJyEhIVN7QkICmzZtynbMBx98wOjRoxk1alSOzjF06FDi4uJCj8TExOOuW5IkSaefsE+lyI2dO3fypz/9iVGjRhEfH5+jMX379iUlJSX0+OGHH05wlZIkSToVFQznyePj4ylQoACbN2/O1L5582bKli2bpf+aNWtYt24dbdq0CbVlZGQAULBgQb7++muSkpIyjYmOjiY6OvoEVC9JkqTTSVjvGEdFRVG/fn3mzp0basvIyGDu3Lk0adIkS/8aNWrwn//8h+XLl4ce1157LZdeeinLly93moQkSZKOWVjvGAMkJyfTtWtXGjRowAUXXMDw4cPZvXs33bt3B6BLly5UqFCBoUOHEhMTQ61atTKNL1GiBECWdkmSJCk3wh6MO3bsyNatW+nfvz+bNm2iXr16zJ49O/SBvPXr1xMZeUpNhZYkSdIpKCIIgiDcReSn1NRU4uLiSElJITY2NtzlSJIk6RDhymveipUkSZIwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAkwGEuSJEmAwViSJEkCDMaSJEkSYDCWJEmSAIOxJEmSBBiMJUmSJMBgLEmSJAEGY0mSJAk4SYLxCy+8QOXKlYmJiaFRo0YsXrz4sH1HjRpFs2bNKFmyJCVLlqRly5ZH7C9JkiTlRNiD8aRJk0hOTmbAgAEsXbqUunXr0qpVK7Zs2ZJt/wULFtC5c2fmz5/PokWLSExM5IorrmDDhg35XLkkSZJOJxFBEAThLKBRo0Y0bNiQESNGAJCRkUFiYiL33HMPffr0Oer49PR0SpYsyYgRI+jSpctR+6emphIXF0dKSgqxsbHHXb8kSZLyVrjyWljvGO/bt48lS5bQsmXLUFtkZCQtW7Zk0aJFOTrGnj172L9/P2ecccaJKlOSJEl/AAXDefJt27aRnp5OQkJCpvaEhAS++uqrHB2jd+/elC9fPlO4/r20tDTS0tJC26mpqcdesCRJkk5bYZ9jfDwef/xxJk6cyLRp04iJicm2z9ChQ4mLiws9EhMT87lKSZIknQrCGozj4+MpUKAAmzdvztS+efNmypYte8SxTz31FI8//jjvvvsuderUOWy/vn37kpKSEnr88MMPeVK7JEmSTi9hDcZRUVHUr1+fuXPnhtoyMjKYO3cuTZo0Oey4J554gsGDBzN79mwaNGhwxHNER0cTGxub6SFJkiQdKqxzjAGSk5Pp2rUrDRo04IILLmD48OHs3r2b7t27A9ClSxcqVKjA0KFDAfjb3/5G//79GT9+PJUrV2bTpk0AFCtWjGLFioXtOiRJknRqC3sw7tixI1u3bqV///5s2rSJevXqMXv27NAH8tavX09k5P9ubL/00kvs27eP9u3bZzrOgAEDePTRR/OzdEmSJJ1Gwr6OcX5zHWNJkqST2x9yHWNJkiTpZGEwliRJkjAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRJgMJYkSZIAg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCThJgvELL7xA5cqViYmJoVGjRixevPiI/SdPnkyNGjWIiYmhdu3azJo1K58qlSRJ0ukq7MF40qRJJCcnM2DAAJYuXUrdunVp1aoVW7Zsybb/Rx99ROfOnbn11ltZtmwZbdu2pW3btqxYsSKfK5ckSdLpJCIIgiCcBTRq1IiGDRsyYsQIADIyMkhMTOSee+6hT58+Wfp37NiR3bt38+9//zvU1rhxY+rVq8fIkSOPer7U1FTi4uJISUkhNjY27y5EkiRJeSJcea1gvp0pG/v27WPJkiX07ds31BYZGUnLli1ZtGhRtmMWLVpEcnJyprZWrVoxffr0bPunpaWRlpYW2k5JSQF+e8IlSZJ08jmY0/L7/m1Yg/G2bdtIT08nISEhU3tCQgJfffVVtmM2bdqUbf9NmzZl23/o0KEMHDgwS3tiYuIxVi1JkqT8sHPnTuLi4vLtfGENxvmhb9++me4wZ2RksH37dkqVKkVERMQJP39qaiqJiYn88MMPTt2QJEmnpPzOM0EQsHPnTsqXL3/Cz/V7YQ3G8fHxFChQgM2bN2dq37x5M2XLls12TNmyZXPVPzo6mujo6ExtJUqUOPaij1FsbKzBWJIkndLyM8/k553ig8K6KkVUVBT169dn7ty5obaMjAzmzp1LkyZNsh3TpEmTTP0B5syZc9j+kiRJUk6EfSpFcnIyXbt2pUGDBlxwwQUMHz6c3bt30717dwC6dOlChQoVGDp0KAC9evWiefPmPP3001x99dVMnDiRzz77jJdffjmclyFJkqRTXNiDcceOHdm6dSv9+/dn06ZN1KtXj9mzZ4c+YLd+/XoiI/93Y/vCCy9k/PjxPPLIIzz00ENUq1aN6dOnU6tWrXBdwhFFR0czYMCALNM5JEmSThV/lDwT9nWMJUmSpJNB2L/5TpIkSToZGIwlSZIkDMaSJEkScBoF48qVKzN8+PAc91+wYAERERHs2LHjhNUkSZKkU0e+B+OIiIgjPh599NFjOu6nn37KHXfckeP+F154IRs3bszXxaNr1KhBdHT0Yb++WpIknd5OVA46eOzp06fnuP+dd95JgQIFmDx58jGf83ST78F448aNocfw4cOJjY3N1PbAAw+E+gZBwIEDB3J03NKlS1OkSJEc1xEVFUXZsmXz5WuhAT744AP27t1L+/btefXVV/PlnEeyf//+cJcgSdIfTm5y0Im0Z88eJk6cyIMPPsgrr7ySL+c8kn379oW7BCAMwbhs2bKhR1xcHBEREaHtr776iuLFi/P2229Tv359oqOj+eCDD1izZg3XXXcdCQkJFCtWjIYNG/Lee+9lOu6hUykiIiL4xz/+Qbt27ShSpAjVqlVjxowZof2HTqUYO3YsJUqU4J133qFmzZoUK1aMK6+8ko0bN4bGHDhwgHvvvZcSJUpQqlQpevfuTdeuXWnbtu1Rr3v06NHcdNNN/OlPf8r2D/DHH3+kc+fOnHHGGRQtWpQGDRrwySefhPa/9dZbNGzYkJiYGOLj42nXrl2maz30FWKJEiUYO3YsAOvWrSMiIoJJkybRvHlzYmJiGDduHD///DOdO3emQoUKFClShNq1azNhwoRMx8nIyOCJJ56gatWqREdHU7FiRR577DEAWrRoQc+ePTP137p1K1FRUVm+nVCSJB05B5UtW5aJEydSs2ZNYmJiqFGjBi+++GJo7L59++jZsyflypUjJiaGSpUqhb4ArXLlygC0a9eOiIiI0PbhTJ48mXPOOYc+ffqwcOFCfvjhh0z709LS6N27N4mJiURHR1O1alVGjx4d2r9y5UquueYaYmNjKV68OM2aNWPNmjUAXHLJJdx3332Zjte2bVu6desW2q5cuTKDBw+mS5cuxMbGht717927N2effTZFihThrLPOol+/fllu5h0uEw0aNCjb77WoV68e/fr1O+LzcdBJOce4T58+PP7446xatYo6deqwa9cuWrduzdy5c1m2bBlXXnklbdq0Yf369Uc8zsCBA+nQoQNffPEFrVu35uabb2b79u2H7b9nzx6eeuopXnvtNRYuXMj69eszvXL729/+xrhx4xgzZgwffvghqampOXrLYufOnUyePJlbbrmFyy+/nJSUFN5///3Q/l27dtG8eXM2bNjAjBkz+Pzzz3nwwQfJyMgAYObMmbRr147WrVuzbNky5s6dywUXXHDU8x6qT58+9OrVi1WrVtGqVSt+/fVX6tevz8yZM1mxYgV33HEHf/rTn1i8eHFoTN++fXn88cfp168fX375JePHjw99+cptt93G+PHjSUtLC/V//fXXqVChAi1atMh1fZIk/ZGNGzeO/v3789hjj7Fq1SqGDBlCv379Qu80P/fcc8yYMYM33niDr7/+mnHjxoUC8KeffgrAmDFj2LhxY2j7cEaPHs0tt9xCXFwcV111Vehm2kFdunRhwoQJPPfcc6xatYq///3vFCtWDIANGzZw8cUXEx0dzbx581iyZAk9evTI8bv8Bz311FPUrVuXZcuWhYJr8eLFGTt2LF9++SXPPvsso0aN4plnngmNOVIm6tGjB6tWrcp07cuWLeOLL74IfaPyUQVhNGbMmCAuLi60PX/+/AAIpk+fftSx5557bvD888+HtitVqhQ888wzoW0geOSRR0Lbu3btCoDg7bffznSuX375JVQLEKxevTo05oUXXggSEhJC2wkJCcGTTz4Z2j5w4EBQsWLF4LrrrjtirS+//HJQr1690HavXr2Crl27hrb//ve/B8WLFw9+/vnnbMc3adIkuPnmmw97fCCYNm1apra4uLhgzJgxQRAEwdq1awMgGD58+BHrDIIguPrqq4O//vWvQRAEQWpqahAdHR2MGjUq27579+4NSpYsGUyaNCnUVqdOneDRRx896nkkSfqjOzQHJSUlBePHj8/UZ/DgwUGTJk2CIAiCe+65J2jRokWQkZGR7fGyywPZ+eabb4JChQoFW7duDYIgCKZNmxZUqVIldNyvv/46AII5c+ZkO75v375BlSpVgn379mW7v3nz5kGvXr0ytV133XWZsk+lSpWCtm3bHrXWJ598Mqhfv35o+2iZ6Kqrrgruuuuu0PY999wTXHLJJUc9z0En5R3jBg0aZNretWsXDzzwADVr1qREiRIUK1aMVatWHfWOcZ06dUI/Fy1alNjYWLZs2XLY/kWKFCEpKSm0Xa5cuVD/lJQUNm/enOlObYECBahfv/5Rr+eVV17hlltuCW3fcsstTJ48mZ07dwKwfPlyzjvvPM4444xsxy9fvpzLLrvsqOc5mkOf1/T0dAYPHkzt2rU544wzKFasGO+8807oeV21ahVpaWmHPXdMTEymqSFLly5lxYoVmd4qkSRJR7d7927WrFnDrbfeSrFixUKP//u//wtNUejWrRvLly+nevXq3Hvvvbz77rvHdK5XXnmFVq1aER8fD0Dr1q1JSUlh3rx5wG+5o0CBAjRv3jzb8cuXL6dZs2YUKlTomM5/0KG5BGDSpEk0bdqUsmXLUqxYMR555JFMee9omej2229nwoQJ/Prrr+zbt4/x48fTo0ePHNdUMHeXkD+KFi2aafuBBx5gzpw5PPXUU1StWpXChQvTvn37o07UPvQXFhEREZqekNP+wXF+Y/aXX37Jxx9/zOLFi+ndu3eoPT09nYkTJ3L77bdTuHDhIx7jaPuzqzO7D9cd+rw++eSTPPvsswwfPpzatWtTtGhR7rvvvtDzerTzwm/TKerVq8ePP/7ImDFjaNGiBZUqVTrqOEmS9D+7du0CYNSoUTRq1CjTvgIFCgBw/vnns3btWt5++23ee+89OnToQMuWLZkyZUqOz5Oens6rr77Kpk2bKFiwYKb2V155hcsuu+y4c0lkZOQx5ZJFixZx8803M3DgQFq1akVcXBwTJ07k6aefzvG527RpQ3R0NNOmTSMqKor9+/fTvn37I47JVHuOe4bRhx9+SLdu3WjXrh21a9embNmyrFu3Ll9riIuLIyEhIdO8lfT0dJYuXXrEcaNHj+biiy/m888/Z/ny5aFHcnJyaBJ7nTp1WL58+WHnP9epU+eIH2YrXbp0pg8Jfvvtt+zZs+eo1/Thhx9y3XXXccstt1C3bl3OOussvvnmm9D+atWqUbhw4SOeu3bt2jRo0IBRo0bl+lWZJEn6TUJCAuXLl+e7776jatWqmR5VqlQJ9YuNjaVjx46MGjWKSZMm8a9//SuUHwoVKkR6evoRzzNr1ix27tzJsmXLMuWSCRMmMHXqVHbs2EHt2rXJyMjg//2//5ftMerUqcP7779/2BWuDs0l6enprFix4qjPwUcffUSlSpV4+OGHadCgAdWqVeP777/Pcu4j5ZKCBQvStWtXxowZw5gxY+jUqVOObvSFxue4ZxhVq1aNqVOn0qZNGyIiIujXr98R7/yeKPfccw9Dhw6latWq1KhRg+eff55ffvnlsEu+7d+/n9deey3bT0nedtttDBs2jJUrV9K5c2eGDBlC27ZtGTp0KOXKlWPZsmWUL1+eJk2aMGDAAC677DKSkpLo1KkTBw4cYNasWaE70C1atGDEiBE0adKE9PR0evfunaO3N6pVq8aUKVP46KOPKFmyJMOGDWPz5s2cc845wG9TJXr37s2DDz5IVFQUTZs2ZevWraxcuZJbb70107X07NmTokWLZlotQ5Ik5dzAgQO59957iYuL48orryQtLY3PPvuMX375heTkZIYNG0a5cuU477zziIyMZPLkyZQtW5YSJUoAv630MHfuXJo2bUp0dDQlS5bMco7Ro0dz9dVXU7du3Uzt55xzDvfffz/jxo3j7rvvpmvXrvTo0YPnnnuOunXr8v3337NlyxY6dOhAz549ef755+nUqRN9+/YlLi6Ojz/+mAsuuIDq1avTokULkpOTmTlzJklJSQwbNixHX6hWrVo11q9fz8SJE2nYsCEzZ85k2rRpmfocLRPBb7mkZs2awG83AXPjlLhjPGzYMEqWLMmFF15ImzZtaNWqFeeff36+19G7d286d+5Mly5daNKkCcWKFaNVq1bExMRk23/GjBn8/PPP2YbFmjVrUrNmTUaPHk1UVBTvvvsuZcqUoXXr1tSuXZvHH3889NbJJZdcwuTJk5kxYwb16tWjRYsWmVaOePrpp0lMTKRZs2bcdNNNPPDAAzla0/mRRx7h/PPPp1WrVlxyySWULVs2y9Jz/fr1469//Sv9+/enZs2adOzYMcs87c6dO1OwYEE6d+582OdCkiQd2W233cY//vEPxowZQ+3atWnevDljx44N3TEuXrw4TzzxBA0aNKBhw4asW7eOWbNmERn5W5x7+umnmTNnDomJiZx33nlZjr9582ZmzpzJDTfckGVfZGQk7dq1C72b/dJLL9G+fXv+8pe/UKNGDW6//XZ2794NQKlSpZg3b15oVa369eszatSo0E25Hj160LVrV7p06ULz5s0566yzuPTSS496/ddeey33338/PXv2pF69enz00UdZllk7WiaC3wL2hRdeSI0aNbJMSzmaiOB4J9H+gWVkZFCzZk06dOjA4MGDw11O2Kxbt46kpCQ+/fTTsLxgkSRJOigIAqpVq8Zf/vIXkpOTczX2lJhKcbL4/vvveffdd2nevDlpaWmMGDGCtWvXctNNN4W7tLDYv38/P//8M4888giNGzc2FEuSpLDaunUrEydOZNOmTTlfu/h3DMa5EBkZydixY3nggQcIgoBatWrx3nvvheax/NF8+OGHXHrppZx99tm5+kSsJEnSiVCmTBni4+N5+eWXs51jfTROpZAkSZI4RT58J0mSJJ1oBmNJkiQJg7EkSZIEGIwlSZIkwGAsSZIkAQZjSZIkCTAYS5IkSYDBWJIkSQIMxpIkSRIA/z98bVxy+vyjXwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Create a Logistic Regression classifier\n",
    "RF = RandomForestClassifier()\n",
    "\n",
    "# Train the classifier\n",
    "RF.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the testing set\n",
    "y_pred = RF.predict(x_test)\n",
    "\n",
    "# Evaluate the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy * 100)\n",
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Assuming you have a trained model, e.g., model = SomeModel().fit(x_train, y_train)\n",
    "# Replace SomeModel with your actual model (e.g., LogisticRegression, RandomForestClassifier, etc.)\n",
    "\n",
    "\n",
    "# Predictions on the training set\n",
    "y_train_pred = RF.predict(x_train)\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "\n",
    "# Predictions on the test set\n",
    "y_test_pred = RF.predict(x_test)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "\n",
    "# Displaying the accuracies\n",
    "print(f\"Training Accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
    "\n",
    "# Check for overfitting\n",
    "if train_accuracy > test_accuracy:\n",
    "    print(\"The model might be overfitting.\")\n",
    "elif train_accuracy < test_accuracy:\n",
    "    print(\"The model might be underfitting.\")\n",
    "else:\n",
    "    print(\"The model is performing well with no clear signs of overfitting.\")\n",
    "\n",
    "\n",
    "accuracies = [train_accuracy, test_accuracy]\n",
    "labels = ['Training Accuracy', 'Test Accuracy']\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(labels, accuracies, color=['blue', 'orange'])\n",
    "plt.ylim(0, 1)  # Set the y-axis range to [0, 1] for clarity\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training vs. Test Accuracy')\n",
    "plt.show()\n",
    "\n",
    "# Alternatively, if you want to use a line plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(labels, accuracies, marker='o', linestyle='-', color='purple')\n",
    "plt.ylim(0, 1)\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training vs. Test Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eadf067-17c1-49cc-936a-874a470db4c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cfa1ff42-7f41-4544-8e76-eb7367368185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.54545454545455\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score \n",
    "clf = GaussianNB()\n",
    "\n",
    "# Train the classifier\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "# Make predictions on the testing set\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "#Evaluate the classifier\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce1ed168-8e40-472b-a664-400136d7524e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.9949\n",
      "Test Accuracy: 0.9955\n",
      "The model might be underfitting.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Assuming you have a trained model, e.g., model = SomeModel().fit(x_train, y_train)\n",
    "# Replace SomeModel with your actual model (e.g., LogisticRegression, RandomForestClassifier, etc.)\n",
    "\n",
    "\n",
    "# Predictions on the training set\n",
    "y_train_pred = clf.predict(x_train)\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "\n",
    "# Predictions on the test set\n",
    "y_test_pred = clf.predict(x_test)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "\n",
    "# Displaying the accuracies\n",
    "print(f\"Training Accuracy: {train_accuracy:.4f}\")\n",
    "print(f\"Test Accuracy: {test_accuracy:.4f}\")\n",
    "\n",
    "# Check for overfitting\n",
    "if train_accuracy > test_accuracy:\n",
    "    print(\"The model might be overfitting.\")\n",
    "elif train_accuracy < test_accuracy:\n",
    "    print(\"The model might be underfitting.\")\n",
    "else:\n",
    "    print(\"The model is performing well with no clear signs of overfitting.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "479fb37b-d354-45b5-8cc5-49677400eae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = [train_accuracy, test_accuracy]\n",
    "labels = ['Training Accuracy', 'Test Accuracy']\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(labels, accuracies, color=['blue', 'orange'])\n",
    "plt.ylim(0, 1)  # Set the y-axis range to [0, 1] for clarity\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training vs. Test Accuracy')\n",
    "plt.show()\n",
    "\n",
    "# Alternatively, if you want to use a line plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(labels, accuracies, marker='o', linestyle='-', color='purple')\n",
    "plt.ylim(0, 1)\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training vs. Test Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30709b11-ff8a-4c50-ad37-27a85e22fdb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        23\n",
      "           1       1.00      1.00      1.00        21\n",
      "           2       1.00      1.00      1.00        20\n",
      "           3       1.00      1.00      1.00        26\n",
      "           4       1.00      1.00      1.00        27\n",
      "           5       1.00      1.00      1.00        17\n",
      "           6       1.00      1.00      1.00        17\n",
      "           7       1.00      1.00      1.00        14\n",
      "           8       0.92      1.00      0.96        23\n",
      "           9       1.00      1.00      1.00        20\n",
      "          10       1.00      1.00      1.00        11\n",
      "          11       1.00      1.00      1.00        21\n",
      "          12       1.00      1.00      1.00        19\n",
      "          13       1.00      1.00      1.00        24\n",
      "          14       1.00      1.00      1.00        19\n",
      "          15       1.00      1.00      1.00        17\n",
      "          16       1.00      1.00      1.00        14\n",
      "          17       1.00      1.00      1.00        23\n",
      "          18       1.00      1.00      1.00        23\n",
      "          19       1.00      1.00      1.00        23\n",
      "          20       1.00      0.89      0.94        19\n",
      "          21       1.00      1.00      1.00        19\n",
      "\n",
      "    accuracy                           1.00       440\n",
      "   macro avg       1.00      1.00      1.00       440\n",
      "weighted avg       1.00      1.00      1.00       440\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e77dc10d-301e-4be0-ac2a-6af85f0cbdf3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ff167d5-b11f-462f-83bf-e617e22ed81b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "39c06247-4fdd-42e0-81ec-ecc0fe6a7fd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([20])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = RF.predict([[90\t,42,\t43,\t20.879744\t,82.002744,6.502985\t,202.935536\t]])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7e1282ce-5cd0-4ddf-a6b7-1b27ea04d8cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# assa dictionary banav o/p sathi\n",
    "#if statement tak \n",
    "#function jo lihilay to pickle kar \n",
    "#prediction ha ahe ki which crop should be grown \n",
    "\n",
    "class_label = {\n",
    "    \"20\": 'rice', \"11\":'maize', \"3\": 'chickpea',\n",
    "    \"9\": 'kidneybeans', \"18\": 'pigeonpeas', \"13\":'mothbeans',\n",
    "    \"14\":'mungbean', \"2\": 'blackgram', \"10\": 'lentil',\n",
    "    \"19\": 'pomegranate', \"1\": 'banana', \"12\":'mango',\n",
    "    \"7\": 'grapes', \"21\": 'watermelon', \"15\":'muskmelon',\n",
    "    \"0\": 'apple', \"16\": 'orange', \"17\": 'papaya',\n",
    "    \"4\": 'coconut', \"6\": 'cotton', \"8\": 'jute', \"5\": 'coffee'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77604e05-517d-4e7b-9020-47ebda3f264d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the 'model' directory if it doesn't exist\n",
    "model_dir = 'model'\n",
    "os.makedirs(model_dir, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b721386-412e-48d6-a931-c6909682c596",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path to save the model\n",
    "model_path = os.path.join(model_dir, 'crop_disease_prediction_model_using_numeric_values.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca8588e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dump the model to the specified path\n",
    "with open(model_path, 'wb') as model_file:\n",
    "    pickle.dump(RF, model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc3f51a2",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
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