Model/随机森林/随机森林-Chocolate Bar Ratings-kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.kaggle.com/rtatman/chocolate-bar-ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/Chocolate Bar Ratings/flavors_of_cacao.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company \\n(Maker-if known)</th>\n",
       "      <th>Specific Bean Origin\\nor Bar Name</th>\n",
       "      <th>REF</th>\n",
       "      <th>Review\\nDate</th>\n",
       "      <th>Cocoa\\nPercent</th>\n",
       "      <th>Company\\nLocation</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Bean\\nType</th>\n",
       "      <th>Broad Bean\\nOrigin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Agua Grande</td>\n",
       "      <td>1876</td>\n",
       "      <td>2016</td>\n",
       "      <td>63%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.75</td>\n",
       "      <td></td>\n",
       "      <td>Sao Tome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Kpime</td>\n",
       "      <td>1676</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>2.75</td>\n",
       "      <td></td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Atsane</td>\n",
       "      <td>1676</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.00</td>\n",
       "      <td></td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Akata</td>\n",
       "      <td>1680</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.50</td>\n",
       "      <td></td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Quilla</td>\n",
       "      <td>1704</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.50</td>\n",
       "      <td></td>\n",
       "      <td>Peru</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company \\n(Maker-if known) Specific Bean Origin\\nor Bar Name   REF  \\\n",
       "0                   A. Morin                       Agua Grande  1876   \n",
       "1                   A. Morin                             Kpime  1676   \n",
       "2                   A. Morin                            Atsane  1676   \n",
       "3                   A. Morin                             Akata  1680   \n",
       "4                   A. Morin                            Quilla  1704   \n",
       "\n",
       "   Review\\nDate Cocoa\\nPercent Company\\nLocation  Rating Bean\\nType  \\\n",
       "0          2016            63%            France    3.75              \n",
       "1          2015            70%            France    2.75              \n",
       "2          2015            70%            France    3.00              \n",
       "3          2015            70%            France    3.50              \n",
       "4          2015            70%            France    3.50              \n",
       "\n",
       "  Broad Bean\\nOrigin  \n",
       "0           Sao Tome  \n",
       "1               Togo  \n",
       "2               Togo  \n",
       "3               Togo  \n",
       "4               Peru  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1795 entries, 0 to 1794\n",
      "Data columns (total 9 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Company \n",
      "(Maker-if known)         1795 non-null   object \n",
      " 1   Specific Bean Origin\n",
      "or Bar Name  1795 non-null   object \n",
      " 2   REF                               1795 non-null   int64  \n",
      " 3   Review\n",
      "Date                       1795 non-null   int64  \n",
      " 4   Cocoa\n",
      "Percent                     1795 non-null   object \n",
      " 5   Company\n",
      "Location                  1795 non-null   object \n",
      " 6   Rating                            1795 non-null   float64\n",
      " 7   Bean\n",
      "Type                         1794 non-null   object \n",
      " 8   Broad Bean\n",
      "Origin                 1794 non-null   object \n",
      "dtypes: float64(1), int64(2), object(6)\n",
      "memory usage: 126.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1795.000000</td>\n",
       "      <td>1795.000000</td>\n",
       "      <td>1795.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1035.904735</td>\n",
       "      <td>2012.325348</td>\n",
       "      <td>3.185933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>552.886365</td>\n",
       "      <td>2.927210</td>\n",
       "      <td>0.478062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>576.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1069.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>3.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1502.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1952.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               REF  Review\\nDate       Rating\n",
       "count  1795.000000   1795.000000  1795.000000\n",
       "mean   1035.904735   2012.325348     3.185933\n",
       "std     552.886365      2.927210     0.478062\n",
       "min       5.000000   2006.000000     1.000000\n",
       "25%     576.000000   2010.000000     2.875000\n",
       "50%    1069.000000   2013.000000     3.250000\n",
       "75%    1502.000000   2015.000000     3.500000\n",
       "max    1952.000000   2017.000000     5.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>1794</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "</table>\n",
       "<p>1795 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company \\n(Maker-if known)  Specific Bean Origin\\nor Bar Name    REF  \\\n",
       "0                          False                              False  False   \n",
       "1                          False                              False  False   \n",
       "2                          False                              False  False   \n",
       "3                          False                              False  False   \n",
       "4                          False                              False  False   \n",
       "...                          ...                                ...    ...   \n",
       "1790                       False                              False  False   \n",
       "1791                       False                              False  False   \n",
       "1792                       False                              False  False   \n",
       "1793                       False                              False  False   \n",
       "1794                       False                              False  False   \n",
       "\n",
       "      Review\\nDate  Cocoa\\nPercent  Company\\nLocation  Rating  Bean\\nType  \\\n",
       "0            False           False              False   False       False   \n",
       "1            False           False              False   False       False   \n",
       "2            False           False              False   False       False   \n",
       "3            False           False              False   False       False   \n",
       "4            False           False              False   False       False   \n",
       "...            ...             ...                ...     ...         ...   \n",
       "1790         False           False              False   False       False   \n",
       "1791         False           False              False   False       False   \n",
       "1792         False           False              False   False       False   \n",
       "1793         False           False              False   False       False   \n",
       "1794         False           False              False   False       False   \n",
       "\n",
       "      Broad Bean\\nOrigin  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "3                  False  \n",
       "4                  False  \n",
       "...                  ...  \n",
       "1790               False  \n",
       "1791               False  \n",
       "1792               False  \n",
       "1793               False  \n",
       "1794               False  \n",
       "\n",
       "[1795 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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     "data": {
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5OcD3gdfanifp4cC9A9U+IiKmZJCe/n8AZ9q+0/Z9wKnATvXc/sAHWHjz1XOBC23PA7D9l6zeiYgYrUEa/YuBZ0h6eE2m8gJgLUkvAm5oNe5t1gMs6URJ50v6QK8LJwxDRMRwDLJO/zJJnwFOBm4H5lHW33+E0qvvdq9tgC2BO4FTJJ1n+5Qu1z6QsuY/SzYjIho00ESu7YNsb2b7GcBfgWsosXjmSbqGEmrhfEmPooRhONX2rTXj1s+BzQa5f0RETM2gq3dasfIfA7wU+K7t1WyvbXttSkO/me0/AScCG9WwDXOAZ1J250ZExIgMlCMXOLJtFc7bbf+tV0Hbf5P0ReAcygTvz20fN+D9IyJiCgZNjL7tYs6v3fH4+5RlmxERMQ2yIzciYowsttGXdLCkW2rUzNaxh0k6WdKV9fMq9fhTJM2tH/Mk7VSPLy/pOEmX1924+w3vW4qIiF4m09P/DvC8jmMfAk6xvS5wSn0MZe3+FrY3qc/5vzppC/B5208ENgWeLun5A9Y9IiKmaLGNvu3TKMsx270YOKR+fQjwklq2tTsXYFnqjtx6/Ff163soMfjXHLTyERExNf2O6T/S9k0A9fNqrROStpJ0CXARsEfbi0Dr/MrAjpR3CF1lR25ExHA0PpFr+yzbG1J23u7THkmzDvUcBnzF9tUTXCOJ0SMihqDfRv9mSY8GqJ9v6Sxg+zLgDuBJbYcPBK60/aU+7xsREQPot9E/Btitfr0b8FMASeu0Jm4lPRZYnxKaAUmfAh4KvKv/6kZExCAms2TzMOAMYH1J8yW9CdgP2EHSlZS4+a0lmNtQ4u7MBY4G3mb7VklrUgKxbUCJxTNX0pub/3YiImIii92Ra3vXHqee3aXs94DvdTk+H+iapDciIkYnO3IjIsZIGv2IiDHSbxiGV9RwCg9I2qLt+MMl/UrS7ZK+1nGdXWsy9AslndCeMD0iIkaj3zAMF1Pi55/WcfxfwEeB97UfrCt6vgw8y/ZGwIXAXn3UNyIiBtBXGAbbl9m+okvZO2yfTmn826l+rCBJwErAjX3XOiIi+jKSMX3b9wJ7UkIz3EhZunlQr/IJwxARMRwjafQlLUVp9DcFVqcM7+zTq3zCMEREDMeoVu9sAmD7D7YNHA48bUT3joiIalSN/g3ABpIeUR/vAFw2ontHRES12B25NQzDdsCqkuYDH6NM7H4VeARwnKS5tv+zlr+GMlG7tKSXAM+1famkfYHTJN0LXAu8vvHvJiIiJqQy2jJzbbPjqTO7ghERM9Dpxz6za+ib7MiNiBgj/e7I/VxNcn6hpKNrNqzWuX0kXSXpCkn/2eV6x7RfKyIiRqffHbknA0+qu2t/T11+KWkDYBdgw/qcb0hasvUkSS8Fbh+82hER0Y9+d+Se1Jb79kwWJDl/MfBD23fb/iNwFfAUAEkrAu8BPtVQ3SMiYoqaGNN/I3B8/XoN4Pq2c/PrMYBPAl8A7lzcBbMjNyJiOAZq9CV9BLgPOLR1qEsxS9oEeILtoydz3ezIjYgYjsWu0+9F0m7AC4Fne8G6z/nAWm3F1qTE2nkqsHldwz8HWE3Sr21v1+/9IyJi6vrq6Ut6HvBB4EW224drjgF2kbSMpHWAdYGzbR9ge3Xba1Py6P4+DX5ExOj1uyN3H2AZ4OQSKZkzbe9h+xJJhwOXUoZ93m77/mFVPiIipiY7ciMiZqHsyI2IiGZ35EpaWtK3ay7ceZK2a3vO5vX4VZK+UjNoRUTECDW6Ixd4C4DtJ1PCJ39BUuseBwC7UyZ31+1yzYiIGLKmd+RuAJxSy9wC/B3YQtKjgZVsn1GXd34XeEkT30BERExe0zty5wEvljSnLtncnLJufw3KGv6W9p26ERExIk3vyD2Y0qCfC3wJ+F0933Wn7gTXTRiGiIghaHRHbh3yeXdbmd8BVwJ/Y8EQECzYqduV7QOBAyFLNiMimtTojlxJy0taoX69A3Cf7Utt3wT8U9LWddXO64CfDl79iIiYikZ35AKrASdKeoCSDP21bZfak7ISaDnKHMDxRETESC220be9a5fDB/Uoew2wfo9z5wJPmkrlIiKiWdmRGxExRtLoR0SMkX7DMHyyhmCYK+kkSavX46+ux1ofD9QEKq0QDQdK+n0N4fCyoX1XERHRVb9hGD5neyPbmwA/A/4HwPahtjepx18LXGN7bn3OR4BbbK9H2bl76sC1j4iIKZnMRO5pktbuOPaPtocr0H2j1a7AYW2P3wg8sT7/AeDWqVY2IiIG0/eYvqRPS7oeeDW1p99hZ2qj34rCCXxS0vmSjpD0yAmunR25ERFD0Hejb/sjtteihGDYq/2cpK2AO2235gHmUHbh/tb2ZsAZwOcnuHYSo0dEDEETq3d+AHROyu7CwkM7fwHuBI6uj48ANmvg3hERMQX9hmFYt+3hi4DL284tAbwC+GHrWI3NcyxlZy/Asyl5dCMiYoT6DcPwAknrAw8A1wJ7tD3lGcB821d3XOqDwPckfQn4M/CGgWsfERFTksToERGzUBKjR0REfzty2869T5Ilrdp2bCNJZ0i6pCZCX7aGXD6u7sS9RNJ+TX8jERGxeP3uyEXSWpTk59e1HZsDfB/Yw/aGlLmAe+vpz9t+IrAp8HRJzx+o5hERMWV9JUav9gc+wMK7cZ8LXGh7Xn3uX2zfb/tO27+qx+4BzmfhTFoRETEC/S7ZfBFwQ6txb7MeYEkn1p23H+jy3JWBHYFTJrh+duRGRAzBlHPkSlqeEjztuT2utw2wJWUz1imSzrN9Sn3uHMqmra90WdL5b8mRGxExHP309B8PrAPMk3QNZZjmfEmPAuYDp9q+tebO/TkL77w9ELjS9pcGqnVERPRlyo2+7Ytsr2Z7bdtrUxr6zWz/CTgR2Kiu1pkDPJO681bSp4CHAu9qqvIRETE1k1myeRglQNr6kuZLelOvsrb/BnwROAeYC5xv+zhJa1KGhDagvCuYK+nNTXwDERExef0mRm8/v3bH4+9Tlm22H5sPdN0dFhERo5MduRERYySNfkTEGOk3MfrHJd3QlgD9BR3PeYyk2yW9r+3YrjUsw4WSTmgP3RAREaPRdxgGYP9WEnTbP+88BxzfelBX8nwZeJbtjYAL6ci2FRERwzdIGIauJL0EuBq4pP1w/VhBkoCVgBunVNOIiBjYIGP6e9WhmoMlrQIgaQVKspR92wvavhfYE7iI0thvABzU68IJwxARMRz9NvoHUHbmbgLcBHyhHt+XMuxze3thSUtRGv1NgdUpwzv79Lp4EqNHRAzHlGPvANi+ufW1pG8CP6sPtwJeLumzwMrAA5L+BZxVn/eH+pzDgQ/1X+2IiOhHX42+pEfbvqk+3Am4GMD2tm1lPg7cbvtrklYHNpD0CNt/psThv2ygmkdExJT1mxh9O0mbUGLpXwO8daJr2L5R0r7AaZLupSRTf/0gFY+IiKlLYvSIiFkoidEjIqL/xOiS3iHpipro/LP12FPadunOk7RTl+sd0y3JekREDN9kJnK/A3wN+G7rgKRnAS8GNrJ9t6TV6qmLgS1s3yfp0ZREK8favq8+76XAQss5IyJidPrdkbsnsJ/tu2uZW+rnO1sNPLAsbUnTJa0IvAf4VAP1joiIPvQ7pr8esK2ksySdKmnL1glJW0m6hLL7do+2F4FPUjZx3bm4i2dHbkTEcPTb6M8BVgG2Bt4PHF5j6mD7LNsbUpKj7yNp2bq88wm2j57MxbMjNyJiOPpt9OcDR7k4G3gAWChUsu3LgDuAJwFPBTavidRPB9aT9Ot+Kx0REf3pt9H/CbA9gKT1gKWBWyWtU8MoI+mxwPrANbYPsL16Ta24DfB729sNWPeIiJiifnfkHgwcXJde3gPsZtuStgE+VHfdPgC8zfatQ6t9RERMSXbkRkTMQtmRGxERafQjIsZJv4nRf9QWbuEaSXPr8R0knVcToJ8nafu252xej18l6SutJZ4RETE6fSVGt71zKyk6cCRwVD11K7Cj7ScDuwHfa3vaAcDuwLr1o1uy9YiIGKKBEqPX3vorgcNq2QtstxKeXwIsK2mZGodnJdtnuMwcfxd4SQP1j4iIKRh0TH9b4GbbV3Y59zLgghqfZw3Khq6W+fVYVwnDEBExHIM2+rtSe/ntJG0IfIYFGbW6jd/3XIqZMAwREcPRV45cgLrz9qXA5h3H1wSOBl7XSoRO6dmv2VZsTeBGIiJipAbp6T8HuNz2v4dtJK0MHAfsY/u3reM1ifo/JW1d5wFeB/x0gHtHREQfJrNk8zDgDGB9SfMlvame2oVFh3b2Ap4AfLRtSWcrwcqewLeAq4A/AMc38Q1ERMTkJQxDRMQslDAMERGRRj8iYpyk0Y+IGCMzfky/KZJ2t33gdNej00ysV+o0OanT5M3Eeo1rncapp7/7dFegh5lYr9RpclKnyZuJ9RrLOo1Tox8RMfbS6EdEjJFxavRn1Nhdm5lYr9RpclKnyZuJ9RrLOo3NRG5ERIxXTz8iYuyl0Y+IGCNp9CMixkjf8fQfzCRtQcn6tTpwF3Ax8AvbXdNCDnCfNYDH0vZzruknZwxJW9s+c5rrsKztf01nHQYl6ent4cR7HYvxJOmllMRSq1GSSgmw7ZVGXpdxmsiV9HrgncAfgfOAW4BlgfWAp1Ma/4/avq6Be30G2Bm4FLi/HrbtFw167SZJOt/2ZvXrM2w/dRrqcBVwM/Ab4DTgt7ZvG3U9BtH+c5zo2ID3OBI4GDje9gNNXXdYakPX6TbgItu3DPnejwDeAqzNwp2uN3YpuxzwGNtXDLE+VwE72r5sWPeYrHHr6a8APN32Xd1OStoEWBcYuNGnJH5fv+YInsnaw68uOx0VsP0ESY+hvPt6IfANSX+3vcl01GcqJD0VeBrwCEnvaTu1ErBkw7c7AHgD8BVJRwDfsX15w/do0puApwK/qo+3A84E1pP0CdvfG+K9f0rpRPyCBZ2uRUjaEfg8sDSwTm0DPjGEztnNM6HBhzFr9G1/fTHn5zZ4u6uBpYCZ3ugvIWkVyvxO6+t/vxA0PeTVTU2x+XRKo78xcAlw+rDv25ClgRUp/0sPaTv+D+DlTd7I9i+AX0h6KCU/9cmSrge+CXzf9r2dz5nmIcYHgP+wfXOtyyMpL1xbUd7RDbPRX972BydR7uPAU4BfQ2kDJK09hPqcK+lHwE9oaxNsHzWEe01orBr9lqm89RvAncBcSaew8C/5nQ3eowkPpQx1tRr689vOGXjcCOpwHXAO8L+29xjB/Rpj+1TgVEnfsX3tsO8n6eHAa4DXAhcAhwLbALtRetLtZbsOMVIa3FFYu9XgV7cA69n+q6RFXqAa9jNJL7D988WUu8/2bSWL61CtRGkTntt2zEAa/RGZ1Fu/AR1TP2Y022tPdx2ATSkN16skfQi4EjjV9kHTVSFJy1KGJzakbdhrgo7BMpIOZNGOxPYN1uko4ImUHvKONfc0wI8kndvlKS9heocYfyPpZ8AR9fHLgNMkrQD8fcj33hv4sKS7gXvpPXF6saRXAUtKWpcy5/e7pitj+w1NX7NfYzWR2yJp7oNhvHgUJL3G9vfr1wutNpG0l+2vjageK1Ia/m0pPVlP9IIk6bHAurZ/USfi5tj+Z4P1OQK4HHgV8Ang1cBltvfuUX4e8P8o75r+3ZGwfV6Dddre9i+nUP544BW2b2+qDlOh0n1+GWXoTpQhuyM9gxodScsDH6H0wAWcCHyy6dVkfXQihmZcG/1PAb+bxFu/fq59uO1XSrqI8vZtIbY3avqeg+hYvbPQapOmV59MUIdzgWUoPazTgdMmGiqR9BZKCNqH2X587aH9P9vPbrBOF9jeVNKFtjeStBRwYq+eu6TzbG/e1P0nqNfTWPTdxHd7lD2SMkcy04cYh6LOT63Lwo1s16EtSSuV0811HDquP6VOxDCN6/DOZN/69XttKKtQHgzU4+tuj4fl+bb/PIXyb6dMvp0FYPtKSas1XKfWmPPfJT0J+BOlse3lWElvA45m4Qa2sYlwSd8DHg/MZeEx+q6NPtM8xDida9MlvZnyv7gm5ee1NXAGsH1HuS0py2AfUh/fBryxyXdo1RNsv0LSi20fIukHlHcVIzeWjb7thyy+VN/Xvql+HvqkXkPc4+tuj4dlCUkHAavbfr6kDYCnTjCmf7fte1qTb5LmDKGuB9ae4kcpDeeK9etedquf3992rOmJ8C2ADSY7PFIbl6Up+1AArui2wmeIPsv0rU3fG9gSONP2syQ9Edi3S7mDgLfZ/g2ApG2AbwNNvyOfaidiaMay0Zf0XcpE7m+Gtc5Z0j9ZtCG6DTgXeK/tq4dx3z48UdKFlF7Y4+vX1MejWLkD8B3KP9pH6uPfAz+i/EN2c6qkDwPLSdoBeBtwbJMVsv2t1r2YxM/B9jpN3r+Hi4FHATctriCApO2AQ4BrKL/PtSTtNsIlm9O5Nv1ftv8lCUnL2L5c0vpdyv2z1eAD2D69/u82rVsn4n+GcJ/FGtcx/e1ZMGn4OMrbv9Nsf7nBe+wL3Aj8gPIPtwvlH/YKYE/b2zV1r0HUCdGeRrQM8RzbW7bG0euxnpPtkpagTIr9e/LN9jcbrtNDKWu4t62Hfk2Z4Ou6U7hOCL6HsrNz9zrPsL7tnzVYp18BmwBns/AQUteNRJLOA17V2mkqaT3gsFHMPdT7fZnyN/8TRrw2XdLRlI1s76IM6fwNWMr2CzrK7Q8sDxxG6aTtXMseWevavoR5VhjLRh9A0pKUt3/PAvYA7rL9xAavf5btrTqOnWl7a0nzbG/c1L2Gof58drF96Aju9WvKKo+TbW8maWvgM7af2aP83p0v0N2ODVinIyk960PqodcCG9vuFlqAuvHmPOB1tp9UVxSd0eQqMUldfx51r0C38hd2LhzodmxYJH27y2GPesVK/bk9FDjB9j0d537V/VlAqWsjS27rxrT/ZfJDmMNje+w+KKsZzgT2B14KrDaEe5wBvJK607V+fWY9N3e6fwZt9VwJ2Af4Ggt6zu8ArgV+OqI6bAb8ljL89VvK8M5GE5Q/v8uxCxqu0yK/o4l+b8C5nfUA5k3z7/ZgyhDZdvXjm8C3p7NOI/7+twHeUL9+BLBOlzJLjqgux9c2YF59PIcSg2jkP5exHNMHLgQ2B55EaWj+rhJsrGtMnj69Gvgy8A3K28YzgdfUHuBeDd5nUN+jvJ09A3gzZSJyaeDFbjYsRU+2z6+9sfUpLzpdJxwl7UpZ8raOpPZVKQ8B/tJwte6StI3t0+u9n06JyNrLPfV361r+8TQcgqO+A/oq8B+U39GSwB3uvRpmT8pKp3dSfq6nUf4eh0rSB2x/VtJX6b5seehLRiV9jDLxvT5lvmgp4PuUPQPtrpL0Y+BgD3f+YVXbh0vaB8D2fZKGtTF0QmPZ6Nt+N/x7Q9AbKH8Uj6KsFW/qHlcDO/Y4PZPiyjzO9pMBJH0LuJUyLj2U9crtWpuNtGg0xvUk4UXHfn9HmcRcFfhC2/F/Ul7Im7QncEgd24fywvj6Ccp/DDiBMll6KKVxmah8P75GmRs6gtKgvY6yDr0rl524X6wfo9RqPLvtEh6VnSg7vc8HsH2jpG6r9jai/EwPqnNFBwM/tP2Phutzh0oIjVanYGtKh7MrSctQhjzXZuE9GZ8YtCJj2ehL2osyQbc5ZRjjYMpqniauPe29nCn6d4/a9v2S/jiKBr96JvBLur84LhKXxGVS+VpK5Mahqu9yNq6bdlhcI2D7ZEnnU9aDC9jb9q1DqNdVkpa0fT/wbUmLhAzQNG8QtN1aSXWn7SPaz0l6xTDv3eYe25bUamRX6KjHHNv31b/1bwLflPQMyoTu/rX3/0nbVzVUn/dQVu08XtJvKcNNEwXk+ynlReE8mn7HWMeXxoqk91Pe6p5n+76Gr72j7WMl7dbtvO1Duh2fLvUt5h2th8BylMBQo9xI02rEJlt+qsMc/dTpf4HP2v57fbwKZantf/covxPwS9fVPZJWBraz/ZMG63Qa8BzgW5R13jcBr3fHogBJj7Z9U6+VWR7RHhKNIMfABPd+H+Vd0A7A/we8EfiB7a+216MuWPivev6xlOHOQymdwv+1vV636/dZpzksZgizrezFtp/U1L0XuvY4NvoAkjZmwXK839ie1+C1lwT2s/3+xRYOJF1HGRr5EaXhnPCPUiVsQ+cwxxNsf2Si502xThe4Lh9tO9azweq2xLTbNQas02MpyWaWBt5NWZHyjV69UUmfcUd44W7Hmibp+cALKBOXP2o7tRJlc9lThnn/tnrswMLLek9uO9dq9K+mxPv/lu0zOp7/lSbemdff2x22b60dlm2AqybqEKgE7/uq7YsGvf8i154Njb6mmFFI0jspsVtawwc7AQe2egEN1emXnsJyLw05gNhMVr/fHSkN+WbAzyjjql3nPiSda3uL9uWHkn5n+2kN1ulCYMs6Lt6q47m2N+xVvnPYRNJFrfmSBuu1NCXSpim9xXsmKNutpz30JZu1Q7UJJcZM+wakfwK/sv23Yd6/S31WBf7S3pmQNJ8y17E8ZYJ+oYbQdiPzIJI+SpnbMfBDyju1X1NyCsyz/a4ez7sUeAIly9/dLHjnPfDvbraM6U81o9Cbga1s3wGl90NZvdJYow9cUFeYHMGC4ZNuk5MLBRCjxFZZkxKxsbEAYjNZXTV1OHB4HUb5MmUnbK/MU3fWxm+upM9ShjlW6FG2X98HTqlrzU15+z/R0Ny5kr4IfL2WfwdlPLYxkv6L8nfxB0ojsI6kt9o+vqPcnpRdyo/Tgh3WUFY5DT1nb33XPE/SDyYawhiG2pPeD/gr8EnKcM2qlFAfr7N9Qi26JGVXrOrnYdmVMgy5PCVvxKNs31mHeuZO8LznD6tCs6Kn36IFGYU+AvTMKFQnuLZ0DZ+qEvb0nCZ7ZZrCxhRJc6kBxLxgR2rjvcSZTGXJ5s6UP/ZzgB/ZPrJH2SkNcwxQp+dRemZQNo71DJBVJwo/2lb+JODTrY5FQ/W5HHhh6/usy0KPc8emwvp/sAplLPtDbaf+6RFkQmurx7q1DhuwcKTLoYX3qEN/H6b8TRxICeZ3pkrsncPa/r9GNbfQHsV2oeG+Hu/EVrL9D0kP63a9Jn5/s6Wnj6aQUYiyRPMsla3aUJJNNLozzlNLmjCKAGIzlqQ/Uno9hwPvX1xDaftalexn2O4WRKspF1DWd7t+3VWdw/mp7ef0KtOQWzpe2K6mZKNaSJ1Mvo3SAUIlAumywIqSVrTdRA7oyfg2ZSnr/pSd72+AoUdunWP7JACVPLxnArjE3mkvN6oIsiurLEkWsJIWLE8W5YWp0w8oEXrPo/zdtdezkQB+s6Knr4UzCn3HCzIK/Xv8t8tzNmdBcofTbPf8p+6jPs+n7HLdgPKLupQSVqBr/P46RPF3yoTkOyhvzS9tcmJyJmv1biZRTpRGZC/K720J4D7KhNfA65c77vVK4HOU8VdRJv3fb/vHPcofA7zWPWLzNFSnAygrTA6n/F29ghLL6bew6NChStLvLwKrU14cHkuJ4d51XmII9T3P9ubt71ol/cb2tot77gD3nFR+CEkPG8W7nh7v+P9tip3DRsyWRn9KGYXqc5YEHsnCGx8G7gHV8fm3Ah9gweaULSjjjN+yfWCX5ywSQKyWffD/ciZBJRDYAcAjXeLWbAS8yPanOsq9m7IqZHfbf6zHHlefe4Lt/Rus0zxgB9u31MePAH7hHjGTJB1OWaN/MgvP4TS2L2MxDcgiQ4f1e9ieUu9NJT0L2NX27k3VaSIq69G3BX5M2Y9xA2VVW7dol03ds7UEuX35MfXxsraXGta9mySp29DTbcC1HnCZ+axo9AE0tYxC76D0GG+mJKNobGa8zrpv09mLqMNPp9v+j0HvMdtIOpUS/uH/2sZcF1mnLOkCSkN8a8fxRwAnudnlkQvNqdQX5nm95lk0A/dltK1ymgdsavsBSWd7dEsmt6Tszl2ZMqn6UMo73rNGcf8HM0lnUlaytcKePxmYBzwc2KM1hNWPWTGmr6lnFNqbEva26XgtUF5IF3nbaPsvHWOKC54gvZDyT/FYyu9kZBujZojlbZ/d8fPp1ptZqrPBB7D9Z5V0hk06QdKJlB2aUCaZe6bXdElYshwlhMUVDdcFAEnrUIb/1mbhzk3X0MqUmFIrUjYiHirpFrr/XIfC9jn1y9uBN9S5qp2pGc9iQtcAb7J9CYBKVM73U9qJoygLBfoyKxp9pphRiLKyZ1hjr/+QtLE7NnuprF3ute7+S5RonxeNy5BOh1vrSpTWlvmX0z1RSM816Ys5N2mSnkAZZnp/nXTbhvIifAZlcUCv5+0IfJ6yomgdSZsAn5igQe7HTygLDo4FFrsfBXgxZQ36uykBAB9KWTs/VCqhK94OrEEJPXByffw+Sm916OG6Z4Enthp8ANuXStrU9tW9Oo+TNVsa/SllFKKsevi1pONYOLlDExsy3gscU8dfWzPwW1JWEb2mx3OuBy4e0wYfSoNwICWL1w2UDSmv7lJuY0ndJnxF25LAAX2JsuSvNTF6FICkLeq5XkH0Pk5Zdvvr+ty5tWfepH/Z/soUyq8G3FSXJrfeiTyS5iOSduoVufUlHlHk1plCiwYTXEjn5HubK+rE/Q/r452B36sEYhto78NsafRXBS6VNKmMQpRNEtdR/hCXbhVvoiIu6daeQmnIXk9pkC4Btrb9px5P+wDw8zq23fSL0IznEpH0OXWt+xKU3unOlOBq7eV6bdZq0tq2F4nYaftcSWtP8Lz7bN/W0Qtr+kX8yyohg09i4b+TXtmdjgDadynfX49t2XC9Ok1b5NYZqNVJWI3yu2gtOHkWpYPQq9F/PWUV37sobcjplHdK99bn9m22NPofn2L5Q2xf036gTjo1wvbNTC3/5acp457LsuBFaNbrGAb4KfALpn8YYKJ3DMtNcO5iSa8Clqybkt5JCQXdpCdT9qFsz4LhHdfH3cxxW5iGuhdkFH9f0xm5dUZpLcmU9DPKEPRN9fGjKbu3ez3vLkr48C90OX37IHWaFY2+e6SLm8CRkl5k+wYAlZCqX6f8U02Hh9l+7jTdezq1DwO8hfKOZ7qHAc6R9BZ35NyV9CYmDqvwDspO8LspG2xOBD41Qfl+7ETpRU92/uLP9e/8GABJL6b0uoetfRhOlAT2/2D8Fii0W7t9/xBl5eAiETw1grDYs2LJpqYYarf26r9Beeu1GSV35Y62rx9NjRepz36U6JJ9z8g/GHVs2lmSGTAMoJLL9GjKxHCrkd+C8ne1U+cQnUoIjz0owbEuAg4adB31BHX7EfCO1t6BSZR/POXd0hr10PWUDWR/GEb9ojdJX6OEem4lYN+FEmnzHR3lhh4We7Y0+t1C7a5r+8MTPOepwP8B/wL+y/afR1HXHnX5JyVg2N2Ut8Zj0SOaaMfkdKsbmVr7BC7ptfmvNsT3UpLwPB+4xj0iJzZQp19TMj2dw+TmrlrPW5Hyvz6WQywzRZ3Ube1GPs320T3KLUkJBT2UsB6zptH3JELtSjqWhd8ybUBZ8fM3WPw/zxTrtB5l1UJr7T31HpMOtzzbaQYkcBlUx7uVOcDZw3rhUglKt4hew5sqgdc+BjyjHjqVsox0aKEiohkaYliPWTGmz+RD7X5+hHU6ghIG95ss2DDWk0pI4XVZOBrhaUOr3QwwotU4w9Y+aXnfoGuoJ2L71Dr81Fp0cPZihnoOpixnfmV9/FpKELQJlxFG86Y6BE0ZgbhIUuNhPWZLT38koXanWKfzbG8+ybJvpuwSXpOyq3hr4Iy8K5j5RvluRVMPAtctm9cix2L4egxB98z2ppITYQ5lldb9lGXMjYT1mBU9fZdQu0tTtqcfxWIyCo3IsZLeRpkUbB9/7RbZb29K7+1M289Sif09zJDB0ZARv1v5CCUPxEJB4CgBzbq5S9I2rhnIJD2d2njE6HlySe3nUBaWvJGyT2UJYC3KO7Sec5RTMSsafU0yo9CItQJwtefJ7RUP+1+2/yUJScu4xP4eWiTCeNBaomM45y+URqGXPYDv1rF9KHNXXQPDxdBNdgj6c5QMZ+u0Jt7rfpbP13PvGrQis2V4Z7IZhU6x/WyNIDn0VKgkc3kD5Re6PeWfcynbL5jOesXMIulzlNU77UHgLlzc33JtNHDJyPQu218aakVjEZMdgpZ0JbBeZ0iWuqLnctvrDlyXWdLon2b7GW2PBZzafqwevxTYk/Ku4FWwcPacCbaz91uvJ7FoqrhekT9bz3km5Q/ihBkwRBUzgBYEgfutFg4C9zfg0Kmsu5d0ne3HDKmqMYHa029tyLrCXfIHS/q97UU2bS3u3JTqMUsa/UllFFKJ3vgmyj/NuR2XcZMTpzVGynaURv/nlDXcp9t+eVP3iPFQt/B/2B0xgVSCwH3Mdq8gcN2udb3ttZquY0xM0nbAIZSQyaKM0+/WuUJP0k+Aozo7h5JeA7yyiWXls6XRn2pGoY/a/uSQ63QRsDFwge2N61K7b7X/g9ZNWd3yYM4BlrY9K+ZcYjDqklCm7dxCyV4mca309KeBpPOAV7nmWqj7eA7rXOEnaQ3KYpS7WDhK73KUHeE3DFqXWdGoeJJ5JiU90fblwHHqko6s4eGdu1wyFd1Xx1RvoWMS1/ZDOur3EEpkvbdSVv1EwBSDwLV1JhY51a18jMRSbkuuY/v36pL4pzbqW0naHtiQ8js73vYpTVVkVjT6mnxGofcAu9M9ct1E0Qr7ca6klSmbs86jRMY7u1vBWu5dlLW7P6Asyxt2zPN48JhSELjOzkTMCOdJOogSZBBKvoieAfxq2I8p5f2erNkyvDOPklHoItoyCvXanj5qKnHYV+oyJrsqJenKzpTdk1/NFvnoNNUgcDHzqCQ/eTsLJuFPo6zeuXvCJw6jLrOk0T/L9lZ9PvdA27sPoU6ivJo/zvYnJD0GeJTts9vK3AH8mbLxYpFgWB6TJCoxOZMNAhczi6QlKEtru87LjNpsafRfRYlbM9mMQu3PHUpkx7qi6AFge9v/UWPrnGR7y7YyH2eC7Eq2sys3YhaQdCiwj+3rprsus2JMnylkFKqvulvbbm2BnlRs8j5sZXszSRcA2P6bOrIW2f74kO4dETPLo4FLVFK6tgdQayyy72TNlkZ/0hmF6oqaLwBPrY+fN6Q63Vt30Rn+HSflgYmfEhGz1Ix51z5bGv15wMpMvtd+kqSXUTZBDGt86yuUybfVJH0aeDnw30O6V0TMYO2LSuoCjr8Mse2Z0GwZ0/81U8gopAWZqlohS4eStKNGy3x2vf4pti/rUmYJ4OW2D2/y3hEx/Woc/f2AvwKfpCzZXJUSKO91tk8YeZ1mSaM/pYxCoyRpeUoohmvdIyVjZ+ygiJgdahz9D1PiaR0IPN/2mbVDeJjtTUddp4nCsj5o1Mb9ckpI0ocAl03U4Kt4jaSP1sdrSXpKE3WR9CJJ10g6X9ILgEuAr1Gy4PQKa3uypPfVejys9dFEfSJiWs2xfZLtI4A/2T4ToEYGmBazpac/1YxCi11OOUBd5lECvj0U+BWwke2rJa1GGeJZJE6KpD92uZRtd4u9HxEPEu1LwjuXhw9rufjizJaJ3KlmFFrscsoBPGD797Uef7R9db3HLZLu6/YE2+s0dO+ImFk2lvQPatyj+jX18UQxlYZmtjT6U80oNMzllEvUdw5LAA/Ur1tRNHvWqZ/Y+xExs3m06TQnZbY0+idIOpGFMwpNlCpxmMspH0qJj9Jq6Nt3BXcdS+sVex9Iox8RjZoVY/oAHRmFTrPdMzRxDX60Dm3LKYGb3T1p+dBNJvZ+REQTHtQ9/fY0cjU7VitD1jMkPd6908gdBbykNYMu6dHAycDmPcoP22Jj70dENOHBvmTzS3SJTgncWc/18hPgCElL1rDHJwL7NFy3qeiMvX8+PWLvR0QM4kE9vDNIGjlJbweeR0m88ta2AGzTqlfs/YiIJjyoh3eYehq597Q/pCQnngtsLWnrJuPX1+3Xl9j+Z338EGAD22d1lJsD3G/bktaiJMfoNSwVETGQB/vwzjmS3tJ5sFcaORbs2H0IsCJlBc9VbceadAAlRWLLHfVYez3fQhm/v7Z+fQplJdEPJX2w4fpERDzoh3dmbBo5SXNtb9Jx7ELbG7U9voSy4ughwGXAY23fWuP1nGN7w1HWOSJmvwf18I7tm4GndaSRO25xaeTqZqwPULLNt2+GajIx+tWS3smC3v3bgKs7ytxj+2/A3yRdZfvWWo87JS02N0BExFQ9qBv9Ftu/osS5maxDgR8BLwT2AHaj5Kpt0h6UTWD/TdmUdQrQmYt3OUmbUobZlq5fi2ncoh0Rs9uDeninX5LOs715+3CLpFNtdw3RPMR6TPhCZftZo6pLRIyHWdHT78O99fNNkv4LuBFYs4kLS/qA7c9K+ipdwi7Yfmfb12nUI2KkxrXR/5SkhwLvBb4KrAS8u6FrX1o/n9vQ9SIiGjOWwzvDJOl7tl8raW/bX57u+kREtBurRl/SVyY63z70MsA9LqVEyTyGEjlT7ec7g7pJErCm7esHvXdExOKMW6N/D3AxcDhlHL+zQT6kgXu8E9iTEjDtho57dM2G1ZpYHvTeERGLM26N/sMpqQx3Bu6jLNs8sq6Vb/peB9jec5Jlvw58x/Y5TdcjIqLdWDX67SStAewKvAf4oO3vNXTdlWz/o1di824x++uQ0HrAtZRwDSpFF+zejYhowliu3pG0GaXB34GSYatbnJ5+/YCy6es8ypLNhYZ36B4n//kN3j8ioqex6ulL2pfSIF8G/BA4wXbXZOWjJmljYNv68De2501nfSJidhq3Rv8BSvybu+qh1jff+HCKpJ2AX9q+rT5eGdjO9k+6lN0beAs18xewE3Cg7a82VZ+ICBi/Rv+xE523fW2D9+oWZfMC25t2KXsh8FTbd9THKwBnZEw/Ipo2bmP613kxr3KStLgyk9QtV0Gvn7eA+9se30/HctKIiCaMW6P/K0lHAj+1fV3roKSlKXHtd6NE6/xOA/c6V9IXga9ThpHeQe8J428DZ0k6uj5+CXBQA3WIiFjIuA3vLAu8EXg1sA7wd0oI4yWBk4Cv257b0L1WAD4KPKceOgn4dGsIp0v5zSgvPAJOs31BE/WIiGg3Vo1+O0lLAasCd9n++xDvs6Lt2xdfMiJi+B7sOXL7Zvte2zcNq8GX9LS66erS+nhjSd8Yxr0iIiZrbBv9Edgf+E/gLwB13f0zprVGETH20ugPUZfImfd3LRgRMSLjtnpnlK6X9DTAdXXQOyk7gSMips3YTuQOm6RVgS9TVu8sAZwI7G37L9NasYgYa2n0IyLGSMb0h0TS4yQdK+nPkm6R9FNJ3SJsRkSMTBr94fkBJUPXo4HVgSOAw6a1RhEx9tLoD49sf8/2ffXj+yyI6hkRMS0ypj8kkvajhHn4IaWx3xlYhhKLp2sGrYiIYUujPySS/jjB6a4J0iMihi2NfkTEGMmYfsMkbSnpUW2PX1dX7nylV7L0iIhRSaPfvP8D7gGQ9AxgP+C7wG3AgdNYr4iIhGEYgiXbJml3puS6PRI4UtLc6atWRER6+sOwpKTWi+mzgV+2ncuLbERMqzRCzTsMOFXSrcBdwG8AJD2BMsQTETFtsnpnCCRtTdmJe1IrPaKk9YAVbZ8/rZWLiLGWRj8iYoxkTD8iYoyk0Y+IGCNp9CMixkga/YiIMfL/A8P1Lbau7Z4AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), cbar=False, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Company \\n(Maker-if known)', 'Specific Bean Origin\\nor Bar Name',\n",
       "       'REF', 'Review\\nDate', 'Cocoa\\nPercent', 'Company\\nLocation', 'Rating',\n",
       "       'Bean\\nType', 'Broad Bean\\nOrigin'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                            887\n",
       "Trinitario                  419\n",
       "Criollo                     153\n",
       "Forastero                    87\n",
       "Forastero (Nacional)         52\n",
       "Blend                        41\n",
       "Criollo, Trinitario          39\n",
       "Forastero (Arriba)           37\n",
       "Criollo (Porcelana)          10\n",
       "Trinitario, Criollo           9\n",
       "Forastero (Parazinho)         8\n",
       "Forastero (Arriba) ASS        6\n",
       "EET                           3\n",
       "Matina                        3\n",
       "Beniano                       3\n",
       "Nacional (Arriba)             3\n",
       "Amazon, ICS                   2\n",
       "Amazon mix                    2\n",
       "Criollo, Forastero            2\n",
       "Trinitario (85% Criollo)      2\n",
       "Criollo (Ocumare 61)          2\n",
       "Trinitario, Forastero         2\n",
       "Criollo (Amarru)              2\n",
       "Forastero (Catongo)           2\n",
       "Nacional                      2\n",
       "Forastero (Amelonado)         1\n",
       "Trinitario, TCGA              1\n",
       "Criollo (Ocumare 77)          1\n",
       "Forastero(Arriba, CCN)        1\n",
       "Trinitario, Nacional          1\n",
       "Forastero (Arriba) ASSS       1\n",
       "Criollo, +                    1\n",
       "Trinitario (Amelonado)        1\n",
       "CCN51                         1\n",
       "Blend-Forastero,Criollo       1\n",
       "Criollo (Wild)                1\n",
       "Trinitario (Scavina)          1\n",
       "Criollo (Ocumare 67)          1\n",
       "Forastero, Trinitario         1\n",
       "Criollo (Ocumare)             1\n",
       "Amazon                        1\n",
       "Name: Bean\\nType, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bean\\nType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bean\\nType'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Values:\n",
      "Company (Maker-if known):  416\n",
      "Specific Bean Origin or Bar Name:  1039\n",
      "Company Location:  60\n",
      "Bean Type:  41\n",
      "Broad Bean Origin 100\n",
      "Review Date:  12\n",
      "Cocoa Percent:  45\n"
     ]
    }
   ],
   "source": [
    "print('Unique Values:')\n",
    "print('Company (Maker-if known): ',df['Company\\xa0\\n(Maker-if known)'].nunique())\n",
    "print('Specific Bean Origin or Bar Name: ', df['Specific Bean Origin\\nor Bar Name'].nunique())\n",
    "print('Company Location: ',df['Company\\nLocation'].nunique())\n",
    "print('Bean Type: ', df['Bean\\nType'].nunique())\n",
    "print('Broad Bean Origin', df['Broad Bean\\nOrigin'].nunique())\n",
    "print('Review Date: ', df['Review\\nDate'].nunique())\n",
    "print('Cocoa Percent: ', df['Cocoa\\nPercent'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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evDaVJHcD9wOHgINVNZ7kHOCTwArgbuDlVfWzuYhPkhaqudxTeFZVraqq8Wb+KmB7Va0EtjfzkqRZdCIdPloLbG6mNwMvmrtQJGlhmqukUMCXkuxKsqEpO6+q9gE03+cOWjHJhiQ7k+ycnJycpXAlaWGYk3MKwMVVtTfJucC2JN8bdsWq2gRsAhgfH6+uApSkhWhO9hSqam/zvR+4AVgN3JtkCUDzvX8uYpOkhWzWk0KSRyd5zOFp4HeA24CtwPqm2nrgs7MdmyQtdHNx+Og84IYkh/v/eFV9Mck3gS1JXgv8AHjZHMQmSQvarCeFqvpH4KkDyn8CPGe245EkPeREuiRVkjTHTAqSpJZJQZLUMilIklpzdfOaTgAf3vw7I2/z1eu/NPI2Jc0e9xQkSS2TgiSpZVKQJLVMCpKklklBktQyKUiSWiYFSVLLpCBJannzmjr3n/56zcjb3PiyL468TUnuKUiS+pgUJEktDx8do4n3/e5I21t25bUjbU+Sjod7CpKk1gmXFJKsSXJnkj1JrprreCRpITmhDh8lWQT8JfBcYAL4ZpKtVXX7dOtOvv+jI49n7PdeOfI2h3HTB5838jaf9bq/GXmbkuafEyopAKuBPVX1jwBJrgPWAtMmBUmaT+7986+OvM3zfv+Z09ZJVY2845lK8lJgTVW9rpl/FfDPq+rKvjobgA3N7K8Ddx5jN4uBH48g3IXUz3zalvnWz3zalvnWz4m8Lb9aVWODFpxoewoZUPawrFVVm4BNM+4g2VlV4zNdfyH2M5+2Zb71M5+2Zb71c7Juy4l2onkCWN43vwzYO0exSNKCc6IlhW8CK5Ocn+RRwDpg6xzHJEkLxgl1+KiqDia5EvjvwCLg2qraPeJuZnzoaQH3M5+2Zb71M5+2Zb71c1Juywl1olmSNLdOtMNHkqQ5ZFKQJLXmZVJIcm2S/UluO8ryJPnz5lEatyZ5Wkf9XJLkF0luaT5/NMK2/0Nfu7clOZTknGbZ3Um+2yzbOWR/y5PclOSOJLuTvGFAnRn93YZs+183bd6a5GtJntq3rKvtOebxGbLdUY/N6Ul2JPlO0+fbBtSZ6dgM0/aox2aYPmcyNsO0O9Kx6Wt3UZJvJ7lxwLIZjc2AdqaMb1T9UFXz7gP8FvA04LajLL8M+AK9+yKeDtzcUT+XADd20fYRdV8A/F3f/N3A4mPsbwnwtGb6McA/ABeM4u82ZNvPAM5upv9lf9sdbs8xj88w7XYwNgHObKZPBW4Gnj6isRmm7VGPzTB9zmRspm131GPTt+4fAB8fFPNMx2ZAO1PGN6p+5uWeQlV9BfjpFFXWAh+pnm8AZyVZ0kE/M3aMbb8C+MRx9revqr7VTN8P3AEsPaLajP5uw7RdVV+rqp81s9+gd49K19szG+2OYmyqqh5oZk9tPkdeITLTsZm27Q7GZpjtmY12j3tsAJIsA54HfPAoVUbyezOEkfQzL5PCEJYC9/TNTzCCH4yj+M1md/YLSS4cdeNJfhlYA1zfV1zAl5LsSu+xIMfa5grgInr/0up33H+3Kdru91p6/+I5rKvtgeMYn+m2ZZRj0xyeuAXYD2yrqpGNzRBt9xvJ2AzZ5zGPzbDbMuL/b94LvAl48CjLR/V7M118I+nnhLpPYRZN+ziNEfkWvWeMPJDkMuAzwMoR9/EC4O+rqn+v4uKq2pvkXGBbku81ex7TSnImvf9R3lhV9x25eMAqQ//dpmn7cJ1n0fvh6X9yV1fbM+PxGWZbGOHYVNUhYFWSs4Abkjy5qvrPN814bIZou9fBCMdmiD5nNDbDbgsjGpskzwf2V9WuJJccrdqgUKfblgGmi28k/SzUPYVZeZxGVd13eHe2qj4PnJpk8Yi7WccRu8BVtbf53g/cQO/ps9NKciq9H7qPVdWnB1SZ8d9tiLZJ8hR6u+Brq+onh8u72p6Zjs8w29IY2dj0rf9z4Mv0/pXb77j/m56i7ZGPzXR9Hu//O1NtS2NUY3Mx8MIkdwPXAc9OcuRz/EfyezNEfKP5XasZnIg4GT7ACo5+Avh5PPyEzI6O+nk8D90guBr4weH54227Wf44eucdHt1X9mjgMX3TX6P35Nnp+grwEeC9U9SZ0d9tyLafCOwBnnFEeZfbc8zjM0y7HYzNGHBWM30G8D+B549obIZpe9RjM0yfMxmbadsd9dgc0e4lDD7RfNy/N8PEN4p+qmp+Hj5K8gl6A7Q4yQTwFnonnaiq/wJ8nt6Z+j3A/wFe01E/LwV+L8lB4J+AddWM3gjaBrgc+FJV/e++Vc+jt9sMvcODH6+qLw7R5cXAq4DvNsdkAd5M7wfheP9uw7T9R8CvANc0sR+s3pMfu9yemYzPMO3CaMdmCbA5vZdQnQJsqaobk/zbvj5nOjbDtD3qsRmmz5mMzTDtwmjHZqARjU2/gfF10I+PuZAkPWShnlOQJA1gUpAktUwKkqSWSUGS1DIpSJJaJgVpCuk9RfPwEzU/19wpO1X9Vc0duIfnX5jkqs4DlUbES1KlKSR5oKrObKY3A/9QVRunqP9qYLyqrpylEKWRmpc3r0kd+TrwFIAkq+k9CO0MejdXvQa4C/jPwBlJngn8SbN8vKquTPJh4D5gnN4du2+qqk8lOQV4H/AvmjZOofd+8k/N3qZJPR4+kobQ3CX7HGBrU/Q94Leq6iJ6d/z+cVX9v2b6k1W1qqo+OaCpJfQeKPd84B1N2YvpPdLknwGvA36zq+2QpuOegjS1M5rHWawAdgHbmvLH0Xukwkp6T6I8dcj2PlNVDwK3JzmvKXsm8NdN+Y+S3DSq4KVj5Z6CNLV/qqpVwK8CjwKuaMrfDtxUVU+m9xjm04ds70DfdI74luacSUEaQlX9Avh94N83j85+HPDDZvGr+6reT+81ncfiq8BLkpzS7D1ccnzRSjNnUpCGVFXfBr5D71n8fwr8SZK/Bxb1VbsJuKC5jPVfDdn09fSehX8b8F/pvcntFyMLXDoGXpIqnQCSnFm9t4z9CrCD3lu2fjTXcWnh8USzdGK4sbkx7lHA200ImivuKUiSWp5TkCS1TAqSpJZJQZLUMilIklomBUlS6/8D7FMK6HrnNj8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = df['Rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Review\\nDate', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = df['Review\\nDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x165685cdc40>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = 'Rating', y= 'Review\\nDate', data = df, kind='kde', color = 'brown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cocoa\\nPercent'] = df['Cocoa\\nPercent'].str.replace('%', '')\n",
    "df['Cocoa\\nPercent'] = df['Cocoa\\nPercent'].str.replace('.', '')\n",
    "df['Cocoa\\nPercent'] = df['Cocoa\\nPercent'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cocoa\\nPercent', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.countplot(x= 'Cocoa\\nPercent', data = df, color = 'brown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalizeIt(percent):\n",
    "    if percent > 100:\n",
    "        percent = int(str(percent)[:2])\n",
    "    return percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Cocoa\\nPercent'] = df['Cocoa\\nPercent'].apply(normalizeIt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cocoa\\nPercent', ylabel='count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.countplot(x= 'Cocoa\\nPercent', data = df, color = 'brown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    375\n",
       "1    275\n",
       "2    300\n",
       "3    350\n",
       "4    350\n",
       "Name: Rating, dtype: int32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rating'] = (df['Rating']* 100).astype(int)\n",
    "df['Rating'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "company = pd.get_dummies(df['Company\\xa0\\n(Maker-if known)'],drop_first=True)\n",
    "sbOrigin = pd.get_dummies(df['Specific Bean Origin\\nor Bar Name'],drop_first=True)\n",
    "companyLocation = pd.get_dummies(df['Company\\nLocation'],drop_first=True)\n",
    "bType = pd.get_dummies(df['Bean\\nType'],drop_first=True)\n",
    "bbOrigin = pd.get_dummies(df['Broad Bean\\nOrigin'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, company, sbOrigin, companyLocation, bType, bbOrigin], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Company\\xa0\\n(Maker-if known)', 'Specific Bean Origin\\nor Bar Name','Company\\nLocation', 'Bean\\nType', \n",
    "         'Broad Bean\\nOrigin'], axis = 1, inplace = True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:,~df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Rating', axis = 1) #Features\n",
    "y = df['Rating']   # Target Variables\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=200)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=200)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['REF', 'Review\\nDate', 'Cocoa\\nPercent', 'Rating', 'AMMA', 'Acalli',\n",
       "       'Adi', 'Aequare (Gianduja)', 'Ah Cacao', 'Akesson's (Pralus)',\n",
       "       ...\n",
       "       'Ven, Trinidad, Ecuador', 'Ven., Indonesia, Ecuad.',\n",
       "       'Ven., Trinidad, Mad.', 'Ven.,Ecu.,Peru,Nic.',\n",
       "       'Venez,Africa,Brasil,Peru,Mex', 'Venezuela, Carribean',\n",
       "       'Venezuela, Dom. Rep.', 'Venezuela, Ghana', 'Venezuela, Java',\n",
       "       'Venezuela/ Ghana'],\n",
       "      dtype='object', length=1574)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Venezuela, dtype: uint8"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Venezuela'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         100       0.00      0.00      0.00         2\n",
      "         150       0.00      0.00      0.00         3\n",
      "         175       0.00      0.00      0.00         1\n",
      "         200       0.20      0.14      0.17         7\n",
      "         225       0.00      0.00      0.00         1\n",
      "         250       0.27      0.07      0.12        41\n",
      "         275       0.20      0.22      0.21        68\n",
      "         300       0.28      0.35      0.31        99\n",
      "         325       0.23      0.24      0.24       100\n",
      "         350       0.28      0.38      0.32       129\n",
      "         375       0.08      0.05      0.06        61\n",
      "         400       0.11      0.04      0.06        27\n",
      "\n",
      "    accuracy                           0.24       539\n",
      "   macro avg       0.14      0.12      0.12       539\n",
      "weighted avg       0.23      0.24      0.23       539\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.304267161410017\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,rfc_pred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Rating', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rating_to_stars(rating):\n",
    "    \n",
    "    rating = int(rating)\n",
    "    \n",
    "    if (rating == 0.0 ):\n",
    "        return 0.0\n",
    "    elif (rating > 0 ) and (rating <= 199 ):\n",
    "        return 1.0\n",
    "    elif (rating >= 200 ) and (rating <= 299 ):\n",
    "        return 2.0\n",
    "    elif (rating >= 300 ) and (rating <= 399 ):\n",
    "        return 3.0\n",
    "    else:\n",
    "        return 4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rating'] = df['Rating'].apply(rating_to_stars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Rating', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Rating', axis = 1)\n",
    "y = df['Rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(min_weight_fraction_leaf=0, n_estimators=5000)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=5000, min_weight_fraction_leaf= 0)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         1.0       0.00      0.00      0.00         6\n",
      "         2.0       0.63      0.16      0.26       117\n",
      "         3.0       0.75      0.97      0.84       389\n",
      "         4.0       0.33      0.04      0.07        27\n",
      "\n",
      "    accuracy                           0.74       539\n",
      "   macro avg       0.43      0.29      0.29       539\n",
      "weighted avg       0.69      0.74      0.67       539\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,rfc_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73.8404452690167\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,rfc_pred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}