Linear Regression (Rotten tomatoes).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d77442b",
   "metadata": {},
   "source": [
    "#                                                     SNEHA S         2213712080024\n",
    "                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5cab335c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e793b9e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import kurtosis, skew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "fcdb2b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d6af6320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>movie_info</th>\n",
       "      <th>critics_consensus</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>authors</th>\n",
       "      <th>actors</th>\n",
       "      <th>original_release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>production_company</th>\n",
       "      <th>tomatometer_status</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_count</th>\n",
       "      <th>audience_status</th>\n",
       "      <th>audience_rating</th>\n",
       "      <th>audience_count</th>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Always trouble-prone, the life of teenager Per...</td>\n",
       "      <td>Though it may seem like just another Harry Pot...</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action &amp; Adventure, Comedy, Drama, Science Fic...</td>\n",
       "      <td>Chris Columbus</td>\n",
       "      <td>Craig Titley, Chris Columbus, Rick Riordan</td>\n",
       "      <td>Logan Lerman, Brandon T. Jackson, Alexandra Da...</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>...</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>49.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>254421.0</td>\n",
       "      <td>43</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m/0878835</td>\n",
       "      <td>Please Give</td>\n",
       "      <td>Kate (Catherine Keener) and her husband Alex (...</td>\n",
       "      <td>Nicole Holofcener's newest might seem slight i...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Catherine Keener, Amanda Peet, Oliver Platt, R...</td>\n",
       "      <td>2010-04-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Sony Pictures Classics</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "      <td>87.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11574.0</td>\n",
       "      <td>44</td>\n",
       "      <td>123</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m/10</td>\n",
       "      <td>10</td>\n",
       "      <td>A successful, middle-aged Hollywood songwriter...</td>\n",
       "      <td>Blake Edwards' bawdy comedy may not score a pe...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy, Romance</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Dudley Moore, Bo Derek, Julie Andrews, Robert ...</td>\n",
       "      <td>1979-10-05</td>\n",
       "      <td>...</td>\n",
       "      <td>Waner Bros.</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>67.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14684.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  rotten_tomatoes_link                                        movie_title  \\\n",
       "0            m/0814255  Percy Jackson & the Olympians: The Lightning T...   \n",
       "1            m/0878835                                        Please Give   \n",
       "2                 m/10                                                 10   \n",
       "\n",
       "                                          movie_info  \\\n",
       "0  Always trouble-prone, the life of teenager Per...   \n",
       "1  Kate (Catherine Keener) and her husband Alex (...   \n",
       "2  A successful, middle-aged Hollywood songwriter...   \n",
       "\n",
       "                                   critics_consensus content_rating  \\\n",
       "0  Though it may seem like just another Harry Pot...             PG   \n",
       "1  Nicole Holofcener's newest might seem slight i...              R   \n",
       "2  Blake Edwards' bawdy comedy may not score a pe...              R   \n",
       "\n",
       "                                              genres          directors  \\\n",
       "0  Action & Adventure, Comedy, Drama, Science Fic...     Chris Columbus   \n",
       "1                                             Comedy  Nicole Holofcener   \n",
       "2                                    Comedy, Romance      Blake Edwards   \n",
       "\n",
       "                                      authors  \\\n",
       "0  Craig Titley, Chris Columbus, Rick Riordan   \n",
       "1                           Nicole Holofcener   \n",
       "2                               Blake Edwards   \n",
       "\n",
       "                                              actors original_release_date  \\\n",
       "0  Logan Lerman, Brandon T. Jackson, Alexandra Da...            2010-02-12   \n",
       "1  Catherine Keener, Amanda Peet, Oliver Platt, R...            2010-04-30   \n",
       "2  Dudley Moore, Bo Derek, Julie Andrews, Robert ...            1979-10-05   \n",
       "\n",
       "   ...      production_company  tomatometer_status tomatometer_rating  \\\n",
       "0  ...        20th Century Fox              Rotten               49.0   \n",
       "1  ...  Sony Pictures Classics     Certified-Fresh               87.0   \n",
       "2  ...             Waner Bros.               Fresh               67.0   \n",
       "\n",
       "  tomatometer_count  audience_status  audience_rating audience_count  \\\n",
       "0             149.0          Spilled             53.0       254421.0   \n",
       "1             142.0          Upright             64.0        11574.0   \n",
       "2              24.0          Spilled             53.0        14684.0   \n",
       "\n",
       "   tomatometer_top_critics_count  tomatometer_fresh_critics_count  \\\n",
       "0                             43                               73   \n",
       "1                             44                              123   \n",
       "2                              2                               16   \n",
       "\n",
       "   tomatometer_rotten_critics_count  \n",
       "0                                76  \n",
       "1                                19  \n",
       "2                                 8  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt1 = pd.read_csv(\"C:/Users/sneha/OneDrive/Desktop/Sem 3/ML/Rotten Tomatoes Dataset/rotten_tomatoes_movies.csv\")\n",
    "rt1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5f7c6036",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie = rt1['movie_title']\n",
    "trating = rt1['tomatometer_rating']\n",
    "tstatus = rt1['tomatometer_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1630acc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>movie_title</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Please Give</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>67.0</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12 Angry Men (Twelve Angry Men)</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20,000 Leagues Under The Sea</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17707</th>\n",
       "      <td>Zoot Suit</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17708</th>\n",
       "      <td>Zootopia</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17709</th>\n",
       "      <td>Zorba the Greek</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17710</th>\n",
       "      <td>Zulu</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17711</th>\n",
       "      <td>Zulu Dawn</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17712 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             movie_title  tomatometer_rating  \\\n",
       "0      Percy Jackson & the Olympians: The Lightning T...                49.0   \n",
       "1                                            Please Give                87.0   \n",
       "2                                                     10                67.0   \n",
       "3                        12 Angry Men (Twelve Angry Men)               100.0   \n",
       "4                           20,000 Leagues Under The Sea                89.0   \n",
       "...                                                  ...                 ...   \n",
       "17707                                          Zoot Suit                56.0   \n",
       "17708                                           Zootopia                98.0   \n",
       "17709                                    Zorba the Greek                80.0   \n",
       "17710                                               Zulu                96.0   \n",
       "17711                                          Zulu Dawn                50.0   \n",
       "\n",
       "      tomatometer_status  \n",
       "0                 Rotten  \n",
       "1        Certified-Fresh  \n",
       "2                  Fresh  \n",
       "3        Certified-Fresh  \n",
       "4                  Fresh  \n",
       "...                  ...  \n",
       "17707             Rotten  \n",
       "17708    Certified-Fresh  \n",
       "17709              Fresh  \n",
       "17710              Fresh  \n",
       "17711             Rotten  \n",
       "\n",
       "[17712 rows x 3 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt = pd.concat([movie, trating, tstatus], axis=1, join='inner')\n",
    "rt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "90f1db10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_title           0\n",
       "tomatometer_rating    0\n",
       "tomatometer_status    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt.dropna(inplace=True)\n",
    "rt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c05c8093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Rotten\n",
       "1    Certified-Fresh\n",
       "2              Fresh\n",
       "3    Certified-Fresh\n",
       "4              Fresh\n",
       "Name: tomatometer_status, dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt['tomatometer_status'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "39b2db1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "rt['tomatometer_status'] = labelencoder.fit_transform(rt['tomatometer_status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "00bcfd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: tomatometer_status, dtype: int32"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rt['tomatometer_status'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "72f90052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        0\n",
       "2        1\n",
       "3        0\n",
       "4        1\n",
       "        ..\n",
       "17707    2\n",
       "17708    0\n",
       "17709    1\n",
       "17710    1\n",
       "17711    2\n",
       "Name: tomatometer_status, Length: 17668, dtype: int32"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tomstat =rt['tomatometer_status']\n",
    "tomstat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6201bcb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Y5UJMc2CMWdbtsM1uHhoEnSVnl/D0+gXLS9fvhxvv/021qxZg7Nnz2LFihX485//jNdff932nBUrVmDlypVYs2YNjhw5gqioKPTv3x9lZWVejJyIvKW5Su6Q7AA3Cp4t+vw01Eq5lyIj8g/hCrlDsgPcuAZf3paNZsGevwYb/QjPf/7zHwwZMgSPPvooAKB169bYtGkTjh49CuDG6M6qVaswZ84cDB8+HACwceNGREZG4qOPPsKzzz7r8J5GoxFGo9H2tU6n88B3QkSeUlFlwYm8Ekzpp0FybBiMZivkEjGO5xXjvcxcVlomcjNWWnZCSkoK9uzZg/PnzwMAvv/+e2RmZuKRRx4BAOTm5qKgoAADBgywvUYmk6F37944dOhQje+5bNkyhIaG2h6xsbHu/0aIyGP0BhNWj0rGibxijNt4FJM+PI7094/gRF4xVo9KRrmRlZaJ3KkxVlpu9CM8s2bNQmlpKdq3bw+xWAyLxYIlS5Zg1KhRAICCggIAQGRkpN3rIiMjcfny5Rrfc/bs2cjIyLB9rdPpmPQQ+RC1UoZVey44/IVZ/fWiIZ28ERaR32iMlZYbfcKzefNm/OMf/8BHH32ExMREnDx5EjNmzEBMTAzGjh1re55IJLJ7nSAIDseqyWQyyGSsw0Hkq0wWa63D6Vk5RTBZrB6OiMi/VFdazqylNAQrLdfgT3/6E1588UX84Q9/QFJSEp566ik899xzWLZsGQAgKioKwP9GeqoVFhY6jPoQkX+obzi9vvNEdGdKKg1YPDSpxkrLi4clocQLu7Qa/QhPRUWFbft5NbFYDKv1xl9o8fHxiIqKwu7du5GcnAwAqKqqwv79+7F8+XKPx0tE3qesZ7i8vvNEdGekEgl+Lq7AnEc7QIDIVmlZBAE/X69A8xDPz7I0+oRn8ODBWLJkCVq1aoXExEScOHECK1euRHp6OoAbU1kzZszA0qVLkZCQgISEBCxduhTBwcEYPXq0l6MnIm9QyQPrHE5XeaHKK5E/UUgD8cr+xlXtvNFf9a+//jrmzp2LSZMmobCwEDExMXj22Wcxb94823NeeOEFVFZWYtKkSbbCg7t27YJKpfJi5ETkLXqjGanaeAiA3S9crUaNVG28V6q8EvmTxljtXCQIguDxT21kdDodQkNDUVpaipCQEG+HQ0R36HBuEVI3HEF6SrytDo8sMAAnrpTgvcxcvJ92D+6NV9f/RkTklGOXiqCUS2usdj73sUSUG6vQLe7Or8GG3L8b/QgPEVFDhQRJUFFlwZq9ObWeJyL3aa6S46WtNVdaXvT5aSwZluTxmBr9Li0iooaSiQOg1dT816NWo4ZMzF99RO5UUU+lZW9UO+cIDxH5nCK9Ec+ktMGjSdGIDJHbWksUlFYiOjQIReVGxLdQejtMIp+lqzQhWCq2m1a+ub0LKy0TEbmAKlgCndGMHdn5dju1emnUSEuJh4pTWkRuFRoswepRydiQlWs3tazVqLF6VLJXppU5rktEPkchDcT6zFyHbekHc4rwXmYuFFL+rUfkTkpZIDZk5dbY3uX9rFwoZZ6/BpnwEJHPKTOY61w/4I0tsUT+pDFeg0x4iMjnNMZOzUT+pL5rzBvXIBMeIvI5jbFTM5E/Ca3nGqzvvDsw4SEinxP8307NNUnRqBHshU7NRP5EUk9pCIkXSkMw4SEin3OtzIC5jyXW2Kl53uBEFOk936mZyJ9c1xuRpo13SHq0GjXStPG4Xm70eEzcqkBEPidYJsGYd7/F8hGdMWtQe+gNFijlYhTqjBj9zrf4+7j7vB0ikU9TBknw9H/bu6Rr4+3au0zbdAKfTOrp8ZiY8BCRz1FIxWgfpcK4jUcdzqVo1FBwSovIrWTiACS3CquxvYu3qp0z4SEin3NNb8D8xxNRpDdCIQu0dWrWG01orpLhmt6AVmqFt8Mk8llF5Y2v2jkTHiLyOQq5BIIAvL43x6FT87zBiVBwlxaRW6mCJNAZGle1cy5aJiKfo5AGYuH20zV2an5l+2lWWiZys8ZY7ZxXPRH5nDKDGSfySjCln6aWxoWstEzkTo2x0jITHiLyOXqDqc7GheVGVlomcqfGWO2cU1pE5HPUSlmtjQs3ZOUiXCHzUmRE/qExVjtnwkNEPqfKYq11OD0rpwhVFquHIyLyL8p6qp0rvVAaggkPEfmc+obT6ztPRHemvMqAxUOTaqx2vmRYEsqrPF/tnGt4iMjn1DdczuahRO6lkMoxf/tpdG3VDGm3VFp+ZftpLBic6PGYmPAQkc9R/Hc4/dYtsQArLRN5gr7Kgr0//oa9P/5W4/mZAy0ejogJDxH5oCL9jeahiz4/bZf03Nw8lJWWidxHV2lCc6UUy0d0RkSIzFbt/FedAbO2nPLKLi0mPETkc+RSNg8l8qYwhQQfPnM/Xvn8tEO18w+fuR8QCR6PiQkPEfkclTywzuahKjl/9RG5k0IaiFlbTtVY7XzR56fx6ojOHo+JVz0R+ZwKkxmLhnbC3G0/OExpLRqahAoTKy0TuVNjrHbOhIeIfE6x3oSZ//4eb4zphpfEASirNEEVJIHZYsXT67/Da0928XaIRD6tMVY7dyrhadasGUQikcNxkUgEuVwOjUaD1NRUpKWl3XGAREQNFRIkwZXiSjy+JqvG89yWTuReaqUMq/ZcqLHaOQAsGtLJ4zE5lfDMmzcPS5YswaBBg3DvvfdCEAQcOXIEX331FSZPnozc3FxMnDgRZrMZ48ePd3XMRER1UskD69yWzjU8RO5lsljrnNIyeaHauUgQhAYvlR4xYgT69++PCRMm2B1fu3Ytdu3ahS1btuD111/HunXrkJ2d7bJg3UWn0yE0NBSlpaUICQnxdjhE5AKXi8oxZ2u2wxqeJcOSEMct6URudfRSEUoqzQ497bQaNdK08QgLCkT31jW3nmiIhty/nUp4lEolTp48CY1GY3c8JycHXbt2hV6vx08//YTOnTujvLy8oW/vcUx4iHzT1eIKlBnMKDOYoJJLoJIHomWzYG+HReTzLv6mx9xPf6ixp51Wo8aiIZ3QpoXyjj+nIfdvp3pphYeHY/v27Q7Ht2/fjvDwcABAeXk5VCqVM29PROQSLZsFo0N0CO6NV6NDdAiTHSIPaYwNfJ2ayJ47dy4mTpyIffv24d5774VIJMLhw4exY8cOvP322wCA3bt3o3fv3i4NloiIiBo/XaUJwVIx0lPia1zD440Gvk5NaQFAVlYW1qxZg3PnzkEQBLRv3x5Tp05Fz549XR2j23FKi4iIyHV+LNDhanFlrWt4WjYLQvuoO7/fNuT+7fRWBa1WC61W6+zLiYiIyEcpZYEOyQ5wYzpLBDStSstWqxU5OTkoLCyE1Wo/F/fAAw/ccWBERETUNJUZzLWu4cnMKWo6lZa//fZbjB49GpcvX8atM2IikQgWi+fbvhMREVHjUF839CbTLX3ChAno3r07vvjiC0RHR9dYdZmIiIj8U0hQ3dXM6zvvDk4lPBcuXMDHH3/sUIeHiIiISCoOgFajrrUOj1TsVFWcO+LUJ953333Iycmp/4lERETkd4r0RoxLiUcvjX015V4aNcalxON6udHjMTk1wjN16lQ8//zzKCgoQFJSEiQS+6Gpzp09v/qaiIiIGgdVkAR6owWDkqKRqo2H0WyFLDAAv+oMEEEEpRca+DpVhycgwHFgSCQSQRCEJrlomXV4iIiIXOdqcQVmbTlV45RWikaNV0d0dknlc7fX4cnNzXUqMCIiIvJ9ZQZznd3Sm8y29Li4OFfHQURERD6i3GjC6lHJ2JCVizV7/7fmV6tRY/WoZJQbG/G29M8++wyDBg2CRCLBZ599VudzH3/88TsOjIiIiJomtUKGv359ocZKywCweEgnj8d02wnP0KFDUVBQgIiICAwdOrTW5zXFNTxERETkOsZ6uqUbG3O39JvbR9zaSoKIiIiomq6i7ikrb3RLd6oOzwcffACj0XEPfVVVFT744IM7DoqIiIiaLlU9lZRVXtiW7lTCk5aWhtLSUofjZWVlSEtLu+OgiIiIqOlSSsVIuaXoYLUUjRpKqdjDETmZ8FTX27nV1atXERoaesdBERERUdNVVG7AoqGdaqy0vHhoEorKDR6PqUHb0pOTkyESiSASifDggw8iMPB/L7dYLMjNzcXDDz/s8iCJiIio6ZBLJbh6vRKP3FJpuVBnxJXrFWgRIvN4TA1KeKp3Z508eRIDBw6EUqm0nZNKpWjdujVGjBjh0gCJiIioaVHKArHowJk6Ky17WoMSnvnz5wMAWrdujZEjR0Iul7slKCIiImq69AZzrdvSM3OKoG8qlZbHjh3r6jiIiIjIR+gM9WxLr+e8OziV8FgsFvz1r3/Fv/71L+Tl5aGqqsru/PXr110SHBERETU9ofVsS6/vvDs4tUtr4cKFWLlyJZ588kmUlpYiIyMDw4cPR0BAABYsWODiEImIiKgpkYgDoK1lW7pWo4ZE7FT6cUec+sQPP/wQ77zzDmbOnInAwECMGjUK7777LubNm4dvv/3W1THi559/xh//+Eeo1WoEBweja9euOHbsmO28IAhYsGABYmJiEBQUhD59+uD06dMuj4OIiIjqV1xuxLzHEh1q8aRo1Jg/OBElFY7Fi93NqYSnoKAASUlJAAClUmkrQvjYY4/hiy++cF10AIqLi6HVaiGRSPDll1/izJkzeO211xAWFmZ7zooVK7By5UqsWbMGR44cQVRUFPr374+ysjKXxkJERET1a6aQYflXZ9G1VTOsH9sdb47phvVju6Nrq2Z49cuzCAtu5NvSq7Vs2RL5+flo1aoVNBoNdu3ahW7duuHIkSOQyVz7TSxfvhyxsbHYsGGD7Vjr1q1t/18QBKxatQpz5szB8OHDAQAbN25EZGQkPvroIzz77LMO72k0Gu1aY+h0OpfGTERE5M+qLFbs/fE37P3xtxrPv/Cw53tyOjXCM2zYMOzZswcAMH36dMydOxcJCQl4+umnkZ6e7tIAP/vsM3Tv3h2///3vERERgeTkZLzzzju287m5uSgoKMCAAQNsx2QyGXr37o1Dhw7V+J7Lli1DaGio7REbG+vSmKtdLa7A2XwdvrtYhB/zdbhaXOGWzyEiImpM6msO6o3moU6N8Lz66qu2///EE08gNjYWWVlZ0Gg0ePzxx10WHABcvHgRb731FjIyMvDSSy/h8OHDmDZtGmQyGZ5++mkUFBQAACIjI+1eFxkZicuXL9f4nrNnz0ZGRobta51O5/Kk53JROV7amm1XhyBFo8aSYUmIUytc+llERESNSX3NQZtE81CTyYS0tDRcvHjRduy+++5DRkaGy5MdALBarejWrRuWLl2K5ORkPPvssxg/fjzeeustu+fd2turtn5fwI0RoJCQELuHK10trnBIdoAbxZbmbM3mSA8REfk0lTywzuahKrlT4y13pMEJj0QiwdatW90RS42io6PRsWNHu2MdOnRAXl4eACAqKgoAbCM91QoLCx1GfTylzGDGibwSTOmnsS3Wei/1Hkzpp8HxvBKUeaHCJBERkae0bBaMJcOSatyltWRYElo2C/Z4TE6lWMOGDcO2bdvspoXcRavV4ty5c3bHzp8/j7i4OABAfHw8oqKisHv3biQnJwMAqqqqsH//fixfvtzt8dVEbzBh9ahkbMjKxZq9ObbjWo0aq0clo9zo+blLIiIiT4pTK/DqiM4oM5hRZjBBJZdAJQ/0SrIDOJnwaDQaLFq0CIcOHcLdd98NhcJ+Tcq0adNcEhwAPPfcc+jZsyeWLl2KJ598EocPH8a6deuwbt06ADemsmbMmIGlS5ciISEBCQkJWLp0KYKDgzF69GiXxdEQaqUMq/ZccJjSqv560ZBO3giLiIjIo7yV3NREJAiC0NAXxcfH1/6GIpHd+h5X+PzzzzF79mxcuHAB8fHxyMjIwPjx423nBUHAwoULsXbtWhQXF+O+++7DG2+8gU6dbi+x0Ol0CA0NRWlpqUvW85wr0GHYm4eQnhKP5NgwGM1WyCViHM8rxnuZudg6qSfaRbl23RAREZG/acj926mEx9e4OuE5dqkIxZVmbMjKtRvl0WrUSNPGo1lwIO6Oq3kxFxEREd2ehty/3drMIiQkxOWjPU1BuELmkOwAN6a0NmTlItwLFSaJiIj8mVsTHn8dPDJarA7JTrWsnCIYLZ6vMElEROTPPL8R3g/oKkwIloprXcPjjQqTRERE/owJjxuEBEvq3JYeEuT5CpNERET+zK1TWv5KKQusdQ3P+1m5UMqYZxIREXmSWxOe2lo7+Dq90VzrGp7MnCLojay0TERE5ElctOwGjbFLLBERkT9zqnlomzZtcObMmXqf++WXX+Kuu+5yKrCmLLSeNTr1nSciIiLXcqp5qNFovK3pqpSUFMhk/ldzRiIOgLaWLrFajRoSMZdOEREReZJTd96pU6di+fLlMJu5FqUmRXoj0rTxDklPdaXl6+VGL0VGRETkn5zaLvTdd99hz5492LVrF5KSkhyah37yyScuCa6pUsolGLvhCNJT4pGujYfRbIUsMAAnrpRg2qYT2DKxp7dDJCIi8itOJTxhYWEYMWKEq2PxGUqpGN1ahdnV4KmWolFDKRV7ISoiIiL/xeahcH3z0BOXixCmkGHuth+QedP29BSNGouHJqGkwoCurdg8lIiI6E405P7tdAU8s9mMb775Bj/99BNGjx4NlUqFX375BSEhIVAqlc6+rU8IlktgEQTMebQDBIhQVmmCKkgCEQSYBSuCZNylRURE5ElOJTyXL1/Gww8/jLy8PBiNRvTv3x8qlQorVqyAwWDA22+/7eo4mxSFNBCztpyqsfhgikaNV0d09kJURERE/suphGf69Ono3r07vv/+e6jV/5uaGTZsGJ555hmXBddUlRnMOJFXgin9NDU2Dy0zcHcbERGRJzmV8GRmZiIrKwtSqdTueFxcHH7++WeXBNaU6Q2mOpuHlhtZaZmIiMiTnKrDY7VaYbFYHI5fvXoVKpXqjoNq6tRKWa3NQzdk5SJc4X/FGImIiLzJqYSnf//+WLVqle1rkUgEvV6P+fPn45FHHnFVbE1WlcVaa/PQrJwiVFmsHo6IiIjIvzk1pfXXv/4Vffv2RceOHWEwGDB69GhcuHABzZs3x6ZNm1wdY5PD5qFERESNi1MJT0xMDE6ePIl//vOfOHbsGKxWK8aNG4cxY8YgKCjI1TE2OSp53dvO6ztPREREruVUwnPgwAH07NkTaWlpSEtLsx03m804cOAAHnjgAZcF2BSp5IFI0ajtig5WS9GooZI7Xf6IiIiInOBUpWWxWIz8/HxERETYHS8qKkJERESNC5obM1dXWv6xQAdduRHRYcHQV1mgqzQhJEgCpVSM/JIKhChkaB91559DRETkz9xeaVkQBIhEIofjRUVFDo1E/ZHVYkJEaDBe3Jptt3i5urVEpdHgxeiIiIj8T4MSnuHDhwO4sSsrNTUVMtn/tldbLBacOnUKPXuyE7hKLndIdgAgM6cIL2/LxrJhSV6KjIiIyD81KOEJDQ0FcGOER6VS2S1QlkqluP/++zF+/HjXRtgE6asstW5Lz8wpgr6qaU35ERERNXUNSng2bNgAAGjdujVmzpzJ6ata1LftvMzAbelERESe5FThwfnz50Mmk+Hrr7/G2rVrUVZWBgD45ZdfoNfrXRpgUxQSxG3pREREjQm7pbuBUiquc1u6Uir2QlRERET+y6kRnupu6cXFxXbreIYNG4Y9e/a4LLimSm8wYMnQJPTSqO2O99KosWRYEvQG7tIiIiLyJHZLdwOxRAJAwCNJ0UjVxsNotkIWGIDCMiMgCP89T0RERJ7iVMLDbul1C5YEYtaWUzXu1ErRqPHqiM5eiIqIiMh/sVu6G5QZzHVuSy8zmD0cERERkX9jt3Q34LZ0IiKixuWOuqVv2rQJx48fZ7f0W3BbOhERUePidNvuoKAgpKenIz093ZXx+ASFVIwH27dAh5hQJMeGwWi2Qi4R43heMc7+UgoFt6UTERF5lNMJz88//4ysrCwUFhbCarXanZs2bdodB9aU6SoNmPtYIuZsy8aavTm24ykaNRYPS4Ku0gCAVaqJiIg8RSQIgtDQF23YsAETJkyAVCqFWq2265wuEolw8eJFlwbpbg1pL387rhSVY/bW7FoLDy4bloRYNRMeIiKiO9GQ+7dTIzzz5s3DvHnzMHv2bAQEOLXRy6fpqyw1JjsAm4cSERF5g1PZSkVFBf7whz8w2akFd2kRERE1Lk5lLOPGjcO///1vV8fiM0Lr2aUVwl1aREREHuXUlNayZcvw2GOP4auvvkJSUhIkt7RKWLlypUuCa6qU8sC6m4fKnV4rTkRERE5w6s67dOlS7Ny5E+3atQMAh0XL/k5vNGNcShs8khSNyBC5bVt6QWklokODoDey0jIREZEnOZXwrFy5Eu+99x5SU1NdHI5vKKs0QYCAHdn5dqM8vTRqpKXEQ881PERERB7l1BoemUwGrVbr6lh8hlopw/rMXIcprYM5RVifmYtwhcxLkREREfknpxKe6dOn4/XXX3d1LD7DZLHW2jw0K6cIJou1xnNERETkHk5NaR0+fBh79+7F559/jsTERIdFy5988olLgmuq6tuWXt95IiIici2nEp6wsDAMHz7c1bH4DFV9zUPrOU9ERESu5VTCs2HDBlfH4VOk4gBoNeoap7W0GjWkYhZsJCIi8qQ7Kgjz22+/4dy5cxCJRGjbti1atGjhqriatCK9Ec+ktMGjtWxLv15uRJsWSm+HSURE5DecSnjKy8sxdepUfPDBB7ZO6WKxGE8//TRef/11BAcHuzTIpkYVJEGZ0VzrtnQlKy0TERF5lFNzKxkZGdi/fz+2b9+OkpISlJSU4NNPP8X+/fvx/PPPuzrGJkcpC6x1W/p7mblQylhpmYiIyJOcuvNu2bIFH3/8Mfr06WM79sgjjyAoKAhPPvkk3nrrLVfF1ySVGcy1bkvPzClCmYGVlomIiDzJ6W7pkZGRDscjIiJQUVFxx0E1dWXslk5ERNSoOJXw9OjRA/Pnz4fBYLAdq6ysxMKFC9GjRw+XBddUhQTX0y2d29KJiIg8yqkprVWrVmHQoEFo2bIlunTpApFIhJMnT0Iul2Pnzp2ujrHJkdWzLV3GbelEREQe5dSdNykpCRcuXMCyZcvQtWtXdO7cGa+++iouXLiAxMREV8dos2zZMohEIsyYMcN2TBAELFiwADExMQgKCkKfPn1w+vRpt8VwO4rKjUjTxkOrUdsd12rUSNPGo6jc6KXIiIiI/JNTIzwHDhxAz549MX78eLvjZrMZBw4cwAMPPOCS4G525MgRrFu3Dp07d7Y7vmLFCqxcuRLvv/8+2rZti8WLF6N///44d+4cVCqVy+O4HUq5BE+/dwTpKfFI18bDaLZCFhiAE1dKMG3TCXwyqadX4iIiIvJXTiU8ffv2RX5+PiIiIuyOl5aWom/fvrBYLC4Jrpper8eYMWPwzjvvYPHixbbjgiBg1apVmDNnjq3VxcaNGxEZGYmPPvoIzz77rEvjuF1KWSC6tQrDmr05DudSNGpuSyciIvIwp+68giBAJBI5HC8qKoJCobjjoG41efJkPProo3jooYfsEp7c3FwUFBRgwIABtmMymQy9e/fGoUOHak14jEYjjMb/TSvpdDqXxqs3mjEupQ0eqaXSst7IbelERESe1KCEp3oURSQSITU1FTKZzHbOYrHg1KlT6NnTtdM1//znP3H8+HEcOXLE4VxBQQEAOGyRj4yMxOXLl2t9z2XLlmHhwoUujfNmZZUmCBBqrbSs57Z0IiIij2pQwhMaGgrgxgiPSqVCUFCQ7ZxUKsX999/vsK7nTly5cgXTp0/Hrl27IJfLa33eraNNtY1AVZs9ezYyMjJsX+t0OsTGxt55wP+lVsqwas8Fh11aB3OKYAWwaEgnl30WERER1a9BCU91l/TWrVtj5syZbpm+utmxY8dQWFiIu+++23bMYrHgwIEDWLNmDc6dOwfgxkhPdHS07TmFhYU1FkasJpPJ7EanXM1ksdZaaTkrpwgmi9Vtn01ERESOnNqWPn/+fLcnOwDw4IMPIjs7GydPnrQ9unfvjjFjxuDkyZNo06YNoqKisHv3bttrqqqqsH//fpdPrTWErp5KyzpOaREREXmU09uFPv74Y/zrX/9CXl4eqqqq7M4dP378jgMDAJVKhU6d7Kd/FAoF1Gq17fiMGTOwdOlSJCQkICEhAUuXLkVwcDBGjx7tkhicUV8l5RB2SyciIvIop0Z4Vq9ejbS0NERERODEiRO49957oVarcfHiRQwaNMjVMdbphRdewIwZMzBp0iR0794dP//8M3bt2uW1GjwAIPlvpeWaaDVqSFhpmYiIyKNEgiAIDX1R+/btMX/+fIwaNQoqlQrff/892rRpg3nz5uH69etYs2aNO2J1G51Oh9DQUJSWliIkJOSO3+9IbhFKDWZsyMq1W8tTXWk5LCgQ3VvXnBARERHR7WnI/dupoYa8vDzbGpmgoCCUlZUBAJ566ils2rTJmbf0KUq5BNM2nUByq2ZYP7Y73hzTDevHdkdyq2aYtukEFDJOaREREXmSU2t4oqKiUFRUhLi4OMTFxeHbb79Fly5dkJubCycGjHyOSl53pWWVnJWWiYiIPMmpO2+/fv2wfft2dOvWDePGjcNzzz2Hjz/+GEePHrUVJ/RnFVVmLHg8EUcuFSNCJbNVWv5VZ8A9rZuhooqVlomIiDzJqYRn3bp1sFpv1JKZMGECwsPDkZmZicGDB2PChAkuDbApqjSaEBosx45Tv+DgLZWWe7RRQ1dp8GJ0RERE/sepRcu+xtWLlq8UlePFrdk1Fh9M0aixbFgSYtXur2NERETkyxpy/3Z6MYnBYMCpU6dQWFhoG+2p9vjjjzv7tj5BX2XBibwSTOmnQXJsmG1K63heMd7LzIW+yrXd5ImIiKhuTiU8X331FZ5++mlcu3bN4ZxIJILF4t83dL3BhNWjkrEhK9du4bJWo8bqUckoN7LSMhERkSc5tS19ypQp+P3vf4/8/HxYrVa7h78nO8CN5qG31uABbvTR2pCVi3CF+/p4ERERkSOnEp7CwkJkZGTU2aDTn7F5KBERUePiVMLzxBNP4JtvvnFxKL6jtJ7mofWdJyIiItdyag3PmjVr8Pvf/x4HDx5EUlISJBL7ysHTpk1zSXBNlaqe5qD1nSciIiLXcirh+eijj7Bz504EBQXhm2++gUgksp0TiURMeOSBSNGokVnLtnRWWiYiIvIsp+rwREVFYdq0aXjxxRcREND0O3+7ug7PuV91CBCJcDT3OiJC5P+rtFxaie7x4bAKAtpF3vnnEBER+TO31+GpqqrCyJEjfSLZcYfyShNUQVLsyM53qLTcvXU4yg1cw0NERORJTmUsY8eOxebNm10di89Qq+RYsP20XbIDAAdzirBw+2molXIvRUZEROSfnBrhsVgsWLFiBXbu3InOnTs7LFpeuXKlS4JrqiqqLLVuS8/MKUIFKy0TERF5lFMJT3Z2NpKTkwEAP/zwg925mxcw+6uyeqas6jtPREREruVUwrNv3z5Xx+FTQoLq3nZe33kiIiJyrTtedXz16lX8/PPProjFZ8jEAdBq1DWe02rUkIm52JuIiMiTnLrzWq1WvPLKKwgNDUVcXBxatWqFsLAwLFq0yKFzuj8qKjciTRvvkPRoNWqkaeNRVG70UmRERET+yakprTlz5mD9+vV49dVXodVqIQgCsrKysGDBAhgMBixZssTVcTYpCpkET793BOkp8UjXxsNotkIWGIATV0owbdMJbJnY09shEhER+RWnEp6NGzfi3XffxeOPP2471qVLF9x1112YNGmS3yc8KnkgurUKw5q9OQ7nWGmZiIjI85ya0rp+/Trat2/vcLx9+/a4fv36HQfV1OmNZqSnxKPXLVNavTRqpKfEQ280eykyIiIi/+TUUEOXLl2wZs0arF692u74mjVr0KVLF5cE1pTpK00QQYRBSdFIvWlK61edASKIoOe2dCIiIo9yKuFZsWIFHn30UXz99dfo0aMHRCIRDh06hCtXrmDHjh2ujrHJUStl+OueH2osPqjVqLF4SCcvREVEROS/nEp44uPjcf78ebzxxhv48ccfIQgChg8fjkmTJsFs5nSN0WLFibwSTOmnQXJsmK156PG8YryXmQujhTvZiIiIPMmpbulisRj5+fmIiIiwO15UVISIiAhYLE2rdYKru6UfvVSEkkozNmTl2o3yVG9LDwsKRPfWNdfpISIiotvTkPu3U4uWa8uR9Ho95HI2xgxXyBySHQDIyinChqxchCtkXoqMiIjIPzVoSisjIwPAjX5Z8+bNQ3BwsO2cxWLBd999h65du7o0wKaoymKttXloVk4RqjilRURE5FENSnhOnDgB4MYIT3Z2NqRSqe2cVCpFly5dMHPmTNdG2ATpKuvehVXfeSIiInKtBiU81U1D09LS8Le//c0l6118kUped3PQ+s4TERGRazm1hmfDhg1MduqgkgcipZbmoay0TERE5Hls2+0G5UYzUmtpHpqqjUc5Ky0TERF5FIca3KC00oRpm07U2jx0Y9o93g6RiIjIrzDhcYPQIAkqqiw1Ng8FgJAgruEhIiLyJE5puYFEHOAwnVVNq1FDIuaPnYiIyJN453WDIr0RabWs4UnTxuN6udFLkREREfknTmm5gVIuwdgNR2pdw7NlYk9vh0hERORXmPC4gUoeiG6twmpcw8Nt6URERJ7HO68bGM1mLBuahJ91BihkYugNFqjkgdAbzbgrVA4jO8oTERF5FBMeNzAYTQiUi/H63gt2PbVSNGosGZoEYxVbSxAREXkSFy27QUiQHC9ty3ZoIJqZU4Q527KhYkd5IiIij2LC4wb6Kkut3dIzc4qgr7J4OCIiIiL/xoTHDerrhl5m4JQWERGRJzHhcYP6KimzWzoREZFnMeFxA6VUXGe3dKVU7OGIiIiI/BsTHje4pjdg0dBODklPikaNRUOTUFRu8FJkRERE/onb0t0gSCbB1euVmNJPg1mD2kNvsEApF6PcaMbV6xVoESLzdohERER+hQmPGyhlgVh04EyNO7VSNGq8OqKzF6IiIiLyX0x43EBvNONEXgmm9NMgOTYMRrMVcokYx/OK8V5mLvRGVlomIiLyJCY8bqA3mLB6VDI2ZOXa9dPSatRYPSoZ5dyWTkRE5FFctOwGaoUMG7JyHaa0snKKsCErF+EKruEhIiLyJCY8bmC0WGuttJyVUwSjxerhiIiIiPwbEx430FXUPWVVXyVmIiIici0mPG6gYqVlIiKiRoUJjxso6qm0rGClZSIiIo9q9AnPsmXLcM8990ClUiEiIgJDhw7FuXPn7J4jCAIWLFiAmJgYBAUFoU+fPjh9+rSXIr5RaXn+4ET00jS3O95L0xzzByfimp6VlomIiDyp0W9L379/PyZPnox77rkHZrMZc+bMwYABA3DmzBkoFAoAwIoVK7By5Uq8//77aNu2LRYvXoz+/fvj3LlzUKlUHo9Z+d8prUFJUUjVtobRbIUsMACFOgMg+t95IiIi8gyRIAiCt4NoiN9++w0RERHYv38/HnjgAQiCgJiYGMyYMQOzZs0CABiNRkRGRmL58uV49tln631PnU6H0NBQlJaWIiQk5I5jvFpcgVlbTtVZablls+A7/hwiIiJ/1pD7d6Of0rpVaWkpACA8PBwAkJubi4KCAgwYMMD2HJlMht69e+PQoUM1vofRaIROp7N7uFKZwVzrtvTMnCKUGVhpmYiIyJOaVMIjCAIyMjKQkpKCTp06AQAKCgoAAJGRkXbPjYyMtJ271bJlyxAaGmp7xMbGujTO+radl7HSMhERkUc1qYRnypQpOHXqFDZt2uRwTiQS2X0tCILDsWqzZ89GaWmp7XHlyhWXxhnCbelERESNSpNJeKZOnYrPPvsM+/btQ8uWLW3Ho6KiAMBhNKewsNBh1KeaTCZDSEiI3cOVuC2diIiocWn0CY8gCJgyZQo++eQT7N27F/Hx8Xbn4+PjERUVhd27d9uOVVVVYf/+/ejZs6enwwVwY1v63McSHZKeFI0a87gtnYiIyOMa/bb0yZMn46OPPsKnn34KlUplG8kJDQ1FUFAQRCIRZsyYgaVLlyIhIQEJCQlYunQpgoODMXr0aK/EHCSVYMy732L5iM6YNag99AYLlHIxCnVGjH7nW/x93H1eiYuIiMhfNfqE56233gIA9OnTx+74hg0bkJqaCgB44YUXUFlZiUmTJqG4uBj33Xcfdu3a5ZUaPACglIrRPkqFcRuPOpxL0aih5JQWERGRRzW5Ojzu4Oo6PCcuF6GZQoa5207jYM412/FemuZYNLQTSioM6Nqq5jU+REREdHsacv9u9CM8TVGzYAkCIKqx0nIAgDBWWiYiIvIoJjxuEBgYWG+lZSIiIvKcRr9LqylipWUiIqLGhQmPG7DSMhERUePChMcNWGmZiIiocWHC4wbKeiotc1s6ERGRZzHhcQOj1YAlQ5NqrLS8ZFgSjFZWWiYiIvIk7tJyA7NFgvIqA14Z0glGsxVllSaogiSQBQagpNwAmZRTWkRERJ7EhMcNlLJALPr8DLelExERNRJMeNygzGCGyWzBgT/1QXmVBbpKE0KCJFBIxZj575Pclk5ERORhTHjcQLCasOKJrpi9NdtulCdFo8aKJ7qi0sg1PERERJ7ERctuoJLLMWdbtsOUVmZOEV7elg2lXO6lyIiIiPwTEx430FdZ6qy0rK+yeDgiIiIi/8aExw1YaZmIiKhxYcLjBqy0TERE1Lgw4XEDRT2VlhWstExERORRTHjc4FqZAXMfS6yx0vK8wYm4pucuLSIiIk/itnQ3CJJJMObdb7F8RGfMGtQeeoMFSrkYhTojRr/zLf4+7j5vh0hERORXmPC4gUoeiPZRKozbeNThXIpGDZWcP3YiIiJP4pSWGxjNZiyuq3momZWWiYiIPIlDDW5QXG6C2WLEnEc7QIDI1jxUBCC/pAKB4gCghbejJCIi8h9MeNxArZDh5U9/qLH4oFajxuIhnbwQFRERkf9iwuMGRosVJ/JKMKWfBsmxYTCarZBLxDieV4z3MnNhtFi9HSIREZFfYcLjBnqDCatHJWNDVi7W7M2xHddq1Fg9Khl6VlomIiLyKC5adgO1QoYNWbkOU1pZOUXYkJULtULmpciIiIj8ExMeNzBarLU2D83KKeKUFhERkYcx4XEDXUXdU1b1NRclIiIi12LC4wYqNg8lIiJqVJjwuIFKHlhn81BWWiYiIvIsJjxuoDeakaqNh/aWpEerUSNVGw+9kZWWiYiIPIlDDW5QWmHCtE0nkJ4Sj3RtPIxmK2SBAThxpQTTNp3A+2n3eDtEIiIiv8KExw1CgiSoqLLY1eC5GdfwEBEReRantNxAKRXXuYZHKRV7OCIiIiL/xoTHDXSVhjq7pZcZDF6KjIiIyD9xSssNQoLkWLD9NLq2aoa0W9bwvLL9NBYMTvR2iERERH6FCY8b6Kss2PPjb9jz4281np850OLhiIiIiPwbEx430FWa0FwpxfIRnRERIoPeYIFKHohfdQbM2nIKZWweSkRE5FFMeNwgTCHBh8/cj1c+P23XUytFo8aHz9wPiAQvRkdEROR/uGjZDRTSQIdkBwAyc4qw6PPTUEiZZxIREXkS77xuUGYw40ReCab00yA5NgxGsxVyiRjH84rxXmYuygystExERORJTHjcQG8wYfWoZGzIyrUrPqjVqLF6VDLKjVzDQ0RE5Emc0nIDtVKGDVm5DlNaWTlF2JCVi3CFzEuRERER+SeO8LhBlcVa55RWlcXq7RCJiIj8ChMeN6hvSkvPbelEREQexSktN1Ar6p7SUnNKi4iIyKOY8LiB0WJ1SHaqZeUUwcgpLSIiIo9iwuMGuoq6p6x0lZzSIiIi8iQmPG6gCpLUfV5e93kiIiJyLSY8bqCSByJFo67xXIpGDZWca8WJiIg8iQmPGxjNZiwemuSQ9KRo1FgyLAlGMystExEReRKHGtzgWpkJr+3Kxl9+3xXlVRaUVZqgCpJAIRVj5r9PYubA9vhdC29HSURE5D+Y8LhBSJAEhy+V4IE/f1Pjea7hISIi8ixOabmBUiqucw2PUir2cERERET+jSM8blBcYcCSoUk49NM1RITIba0lfi2tRE9NcxRXGBCrVng7TCIiIr/BhMcNpBIJ8oorsCM7HwdvKkDYK6E5WoYHo4WKlZaJiIg8iVNabqCUBeLt/T/ZJTsAcPDCNazd/xOUMuaZREREnuQzd94333wTf/7zn5Gfn4/ExESsWrUKvXr18koseqMZfRPC8eqwJOirLNBVmhASJIFSKsbOH36G3sht6URERJ7kEwnP5s2bMWPGDLz55pvQarVYu3YtBg0ahDNnzqBVq1Yej0cpNuGhxLvw4tZsu55aKRo1Fg9NQqBg8HhMRERE/swnprRWrlyJcePG4ZlnnkGHDh2watUqxMbG4q233vJKPEKAHHO2ZTs0EM3MKcLL27IhBMi9EhcREZG/avIJT1VVFY4dO4YBAwbYHR8wYAAOHTpU42uMRiN0Op3dw5X0VZZau6Vn5hRBX2Vx6ecRERFR3Zp8wnPt2jVYLBZERkbaHY+MjERBQUGNr1m2bBlCQ0Ntj9jYWJfGVF839DIDu6UTERF5UpNPeKqJRCK7rwVBcDhWbfbs2SgtLbU9rly54tJYQtgtnYiIqFFp8ouWmzdvDrFY7DCaU1hY6DDqU00mk0Emc18tnOpu6Zk1TGuxWzoREZHnNfkRHqlUirvvvhu7d++2O75792707NnTKzG1bBaMJcNq75beslmwV+IiIiLyVz4x1JCRkYGnnnoK3bt3R48ePbBu3Trk5eVhwoQJXospTq3AqyM6o8xgRpnBBJVcApU8kMkOERGRF/hEwjNy5EgUFRXhlVdeQX5+Pjp16oQdO3YgLi7Oq3ExuSEiImocRIIgCN4Owtt0Oh1CQ0NRWlqKkJAQb4dDREREt6Eh9+8mv4aHiIiIqD5MeIiIiMjnMeEhIiIin8eEh4iIiHweEx4iIiLyeUx4iIiIyOcx4SEiIiKfx4SHiIiIfB4THiIiIvJ5PtFa4k5VF5vW6XRejoSIiIhuV/V9+3aaRjDhAVBWVgYAiI2N9XIkRERE1FBlZWUIDQ2t8znspQXAarXil19+gUqlgkgkcul763Q6xMbG4sqVK+zTReQFvAaJvMud16AgCCgrK0NMTAwCAupepcMRHgABAQFo2bKlWz8jJCSEv2yJvIjXIJF3uesarG9kpxoXLRMREZHPY8JDREREPo8Jj5vJZDLMnz8fMpnM26EQ+SVeg0Te1ViuQS5aJiIiIp/HER4iIiLyeUx4iIiIyOcx4SEiIiKfx4SHiBqVb775BiKRCCUlJbZj27Ztg0ajgVgsxowZM/D+++8jLCzsjj9LJBJh27Ztd/w+rnLp0iWIRCKcPHnS26EQeUVqaiqGDh3qlvdmwnOT1NRUiEQiiEQiBAYGolWrVpg4cSKKi4tv6/W1/bJy5z8gkScUFBRg6tSpaNOmDWQyGWJjYzF48GDs2bPnjt63T58+mDFjht2xnj17Ij8/366Y2LPPPosnnngCV65cwaJFizBy5EicP3/+jj77dlRf07c+/vjHP7r9s4kai5vvjTc/cnJyvB1ag7DS8i0efvhhbNiwAWazGWfOnEF6ejpKSkqwadMmb4dG5BWXLl2CVqtFWFgYVqxYgc6dO8NkMmHnzp2YPHkyfvzxxwa/p8lkgkQiqfGcVCpFVFSU7Wu9Xo/CwkIMHDgQMTExtuNBQUEN/2ac9PXXXyMxMbHOzxYEARaLBYGB/LVKvqf63nizFi1a2H1dVVUFqVTqybAahCM8t5DJZIiKikLLli0xYMAAjBw5Ert27QJwo+fWK6+8gpYtW0Imk6Fr16746quvbK+Nj48HACQnJ0MkEqFPnz5YsGABNm7ciE8//dSWFX/zzTcAgJ9//hkjR45Es2bNoFarMWTIEFy6dMn2ftUjQ3/5y18QHR0NtVqNyZMnw2QyeeznQTRp0iSIRCIcPnwYTzzxBNq2bYvExERkZGTg22+/BQCUlpbi//7v/xAREYGQkBD069cP33//ve09FixYgK5du+K9996zjRKNHTsW+/fvx9/+9jfbtXHp0iW7Ka1vvvkGKpUKANCvXz/b9VPTlNb27dtx9913Qy6Xo02bNli4cCHMZrPt/IULF/DAAw9ALpejY8eO2L17923/DNRqNaKiomyP0NBQW5w7d+5E9+7dIZPJcPDgQQiCgBUrVqBNmzYICgpCly5d8PHHH9veq7i4GGPGjEGLFi0QFBSEhIQEhxvJxYsX0bdvXwQHB6NLly74z3/+c9uxErlD9b3x5seDDz6IKVOmICMjA82bN0f//v0BAGfOnMEjjzwCpVKJyMhIPPXUU7h27ZrtvT7++GMkJSUhKCgIarUaDz30EMrLy+0+zx33Pf4pUoeLFy/iq6++sv0l+re//Q2vvfYa1q5di+TkZLz33nt4/PHHcfr0aSQkJODw4cO49957bX8NSqVSSKVSnD17FjqdzvZLLTw8HBUVFejbty969eqFAwcOIDAwEIsXL8bDDz+MU6dO2bLkffv2ITo6Gvv27UNOTg5GjhyJrl27Yvz48V77uZD/uH79Or766issWbIECoXC4XxYWBgEQcCjjz6K8PBw7NixA6GhoVi7di0efPBBnD9/HuHh4QCAnJwc/Otf/8KWLVsgFosRFxeHCxcuoFOnTnjllVcA3PiL8eakv2fPnjh37hzatWuHLVu2oGfPnggPD7d7DgDs3LkTf/zjH7F69Wr06tULP/30E/7v//4PADB//nxYrVYMHz4czZs3x7fffgudTucwleasF154AX/5y1/Qpk0bhIWF4eWXX8Ynn3yCt956CwkJCThw4AD++Mc/okWLFujduzfmzp2LM2fO4Msvv0Tz5s2Rk5ODyspKu/ecM2cO/vKXvyAhIQFz5szBqFGjkJOTw9EjanQ2btyIiRMnIisrC4IgID8/H71798b48eOxcuVKVFZWYtasWXjyySexd+9e5OfnY9SoUVixYgWGDRuGsrIy2x8K1dx23xPIZuzYsYJYLBYUCoUgl8sFAAIAYeXKlYIgCEJMTIywZMkSu9fcc889wqRJkwRBEITc3FwBgHDixAmH9x0yZIjdsfXr1wvt2rUTrFar7ZjRaBSCgoKEnTt32l4XFxcnmM1m23N+//vfCyNHjnTVt0xUp++++04AIHzyySe1PmfPnj1CSEiIYDAY7I7/7ne/E9auXSsIgiDMnz9fkEgkQmFhod1zevfuLUyfPt3u2L59+wQAQnFxsSAIglBcXCwAEPbt22d7zoYNG4TQ0FDb17169RKWLl1q9z5///vfhejoaEEQBGHnzp2CWCwWrly5Yjv/5ZdfCgCErVu31vq9VV/TQUFBgkKhsD2OHz9ui3Pbtm225+v1ekEulwuHDh2ye59x48YJo0aNEgRBEAYPHiykpaXV+Xnvvvuu7djp06cFAMLZs2drjZPInW6+N1Y/nnjiCaF3795C165d7Z47d+5cYcCAAXbHrly5IgAQzp07Jxw7dkwAIFy6dKnWz3LXfY9/Ltyib9++eOutt1BRUYF3330X58+fx9SpU6HT6fDLL79Aq9XaPV+r1doN3d+uY8eOIScnxzZcX81gMOCnn36yfZ2YmAixWGz7Ojo6GtnZ2Q3+PCJnCP/9q0skEtX6nGPHjkGv10OtVtsdr6ystPtvOS4uzmHO31WOHTuGI0eOYMmSJbZjFosFBoMBFRUVOHv2LFq1aoWWLVvazvfo0cPuPQYNGoSDBw/aYj19+rTt3ObNm9GhQwfb17GxsbZppu7du9uOnzlzBgaDwTa0X62qqgrJyckAgIkTJ2LEiBE4fvw4BgwYgKFDh6Jnz552z+/cubPt/0dHRwMACgsL0b59+wb8VIhcp/reWE2hUGDUqFF2//0DN67Fffv2QalUOrzHTz/9hAEDBuDBBx9EUlISBg4ciAEDBuCJJ55As2bNbM9z132PCc8tFAoFNBoNAGD16tXo27cvFi5ciD/96U8AHH/xC4JQ582gNlarFXfffTc+/PBDh3M33xRuXdgpEolgtVob/HlEzkhISIBIJMLZs2dr3WlotVoRHR1tW5t2s5vX2dQ0JeYqVqsVCxcuxPDhwx3OyeVyu+Hyardet++++65taunW6y42Ntb2e+FWN39f1dfmF198gbvuusvuedV9hAYNGoTLly/jiy++wNdff40HH3wQkydPxl/+8hfbc2/+/Oo4ed2TN918b7z1+M2sVisGDx6M5cuXOzw3OjoaYrEYu3fvxqFDh7Br1y68/vrrmDNnDr777jvbOlh33feY8NRj/vz5GDRoECZOnIiYmBhkZmbigQcesJ0/dOgQ7r33XgCwrbuxWCx27yGVSh2OdevWDZs3b7Yt8iRqjMLDwzFw4EC88cYbmDZtmsMvt5KSEnTr1g0FBQUIDAxE69atG/T+NV0bzujWrRvOnTtXa1LSsWNH5OXl4ZdffrHt9Lp1IfCtCYozOnbsCJlMhry8PPTu3bvW57Vo0QKpqalITU1Fr1698Kc//cku4SFqqrp164YtW7agdevWta45E4lE0Gq10Gq1mDdvHuLi4rB161ZkZGS4NTbu0qpHnz59kJiYiKVLl+JPf/oTli9fjs2bN+PcuXN48cUXcfLkSUyfPh0AEBERgaCgIHz11Vf49ddfUVpaCgBo3bo1Tp06hXPnzuHatWswmUwYM2YMmjdvjiFDhuDgwYPIzc3F/v37MX36dFy9etWb3zKRnTfffBMWiwX33nsvtmzZggsXLuDs2bNYvXo1evTogYceegg9evTA0KFDsXPnTly6dAmHDh3Cyy+/jKNHj9b53q1bt8Z3332HS5cu4dq1a07/FTdv3jx88MEHWLBgAU6fPo2zZ89i8+bNePnllwEADz30ENq1a4enn34a33//PQ4ePIg5c+Y49Vl1UalUmDlzJp577jls3LgRP/30E06cOIE33ngDGzdutMX66aefIicnB6dPn8bnn39uN11G1JRNnjwZ169fx6hRo3D48GFcvHgRu3btQnp6OiwWC7777jssXboUR48eRV5eHj755BP89ttvHrkGmPDchoyMDLzzzjsYNmwYnn/+eTz//PNISkrCV199hc8++wwJCQkAgMDAQKxevRpr165FTEwMhgwZAgAYP3482rVrh+7du6NFixbIyspCcHAwDhw4gFatWmH48OHo0KED0tPTUVlZyREfalTi4+Nx/Phx9O3bF88//zw6deqE/v37Y8+ePXjrrbcgEomwY8cOPPDAA0hPT0fbtm3xhz/8AZcuXUJkZGSd7z1z5kyIxWJ07NgRLVq0QF5enlMxDhw4EJ9//jl2796Ne+65B/fffz9WrlyJuLg4AEBAQAC2bt0Ko9GIe++9F88884zdeh9XWrRoEebNm4dly5ahQ4cOGDhwILZv324brpdKpZg9ezY6d+6MBx54AGKxGP/85z/dEguRp8XExCArKwsWiwUDBw5Ep06dMH36dISGhiIgIAAhISE4cOAAHnnkEbRt2xYvv/wyXnvtNQwaNMjtsYmEmia3iYiIiHwIR3iIiIjI5zHhISIiIp/HhIeIiIh8HhMeIiIi8nlMeIiIiMjnMeEhIiIin8eEh4iIiHweEx4iIiLyeUx4iIiIyOcx4SHyMX369MGMGTO8HQYAIDU1tdYu697QWOJx9t+oscRP1BQx4SEiaqCqqipvh0BEDSUQkc8YO3asAMDukZubK3zzzTfCPffcI0ilUiEqKkqYNWuWYDKZbK/r3bu3MGXKFGH69OlCWFiYEBERIaxdu1bQ6/VCamqqoFQqhTZt2gg7duywvcZsNgvp6elC69atBblcLrRt21ZYtWqV7fz8+fMdYtm3b58gCIJw6tQpoW/fvoJcLhfCw8OF8ePHC2VlZXbfx5AhQ4QlS5YIERERQmhoqLBgwQLBZDIJM2fOFJo1aybcddddwvr16+2+/6tXrwpPPvmkEBYWJoSHhwuPP/64kJubW288db3u5niWLl0qREdHC3FxcfX+W7zxxhuCRqMRZDKZEBERIYwYMaLOfyNnf5779u0TAAjFxcW25544ccL2voIgCJcuXRIee+wxISwsTAgODhY6duwofPHFF/V+D0S+hAkPkQ8pKSkRevToIYwfP17Iz88X8vPzhatXrwrBwcHCpEmThLNnzwpbt24VmjdvLsyfP9/2ut69ewsqlUpYtGiRcP78eWHRokVCQECAMGjQIGHdunXC+fPnhYkTJwpqtVooLy8XBEEQqqqqhHnz5gmHDx8WLl68KPzjH/8QgoODhc2bNwuCIAhlZWXCk08+KTz88MO2WIxGo1BeXi7ExMQIw4cPF7Kzs4U9e/YI8fHxwtixY23xjB07VlCpVMLkyZOFH3/8UVi/fr0AQBg4cKCwZMkSW4wSiUTIy8sTBEEQysvLhYSEBCE9PV04deqUcObMGWH06NFCu3btBKPRWGc8db2uOh6lUik89dRTwg8//CBkZ2fX+e9w5MgRQSwWCx999JFw6dIl4fjx48Lf/va3Wv+NzGaz0z/P20l4Hn30UaF///7CqVOnhJ9++knYvn27sH//fqf/OyNqipjwEPmY3r17C9OnT7d9/dJLLwnt2rUTrFar7dgbb7whKJVKwWKx2F6TkpJiO282mwWFQiE89dRTtmP5+fkCAOE///lPrZ89adIk20iGIPxvZORm69atE5o1aybo9XrbsS+++EIICAgQCgoKbK+Li4uzxScIgtCuXTuhV69eDjFu2rRJEARBWL9+vcP3aTQahaCgIGHnzp21xnO7r4uMjLQlQPXZsmWLEBISIuh0uhrP3/pvVJvb+XneTsKTlJQkLFiw4LZiJ/JVgZ6ZOCMibzl79ix69OgBkUhkO6bVaqHX63H16lW0atUKANC5c2fbebFYDLVajaSkJNuxyMhIAEBhYaHt2Ntvv413330Xly9fRmVlJaqqqtC1a9d64+nSpQsUCoVdPFarFefOnbN9TmJiIgIC/rfMMDIyEp06dXKIsTqeY8eOIScnByqVyu7zDAYDfvrpp1rjud3XJSUlQSqV1vm9Vevfvz/i4uLQpk0bPPzww3j44YcxbNgwBAcH1/k6Z36et2PatGmYOHEidu3ahYceeggjRoyw+/cm8gdctEzk4wRBsEt2qo8BsDsukUjsniMSieyOVT/XarUCAP71r3/hueeeQ3p6Onbt2oWTJ08iLS2t3gW9NcVz62fcTjzVx6rjsVqtuPvuu3Hy5Em7x/nz5zF69Oha47nd192coNVHpVLh+PHj2LRpE6KjozFv3jx06dIFJSUltb7G2Z9ndVJY/W8KACaTye45zzzzDC5evIinnnoK2dnZ6N69O15//fXb/n6IfAFHeIh8jFQqhcVisX3dsWNHbNmyxS7ROHToEFQqFe666y6nP+fgwYPo2bMnJk2aZDt260jKrbFUx7Nx40aUl5fbkoisrCwEBASgbdu2TsfTrVs3bN68GREREQgJCanxOTXFczuvc0ZgYCAeeughPPTQQ5g/fz7CwsKwd+9eDB8+vMY4nP15tmjRAgCQn5+PZs2aAQBOnjzpEE9sbCwmTJiACRMmYPbs2XjnnXcwdepUV3yrRE0CR3iIfEzr1q3x3Xff4dKlS7h27RomTZqEK1euYOrUqfjxxx/x6aefYv78+cjIyLCbMmoojUaDo0ePYufOnTh//jzmzp2LI0eOOMRy6tQpnDt3DteuXYPJZMKYMWMgl8sxduxY/PDDD9i3bx+mTp2Kp556yjad5YwxY8agefPmGDJkCA4ePIjc3Fzs378f06dPx9WrV+uMp77XNdTnn3+O1atX4+TJk7h8+TI++OADWK1WtGvXzhbHzf9GVqvV6Z+nRqNBbGwsFixYgPPnz+OLL77Aa6+9Zve6GTNmYOfOncjNzcXx48exd+9edOjQwanvjaipYsJD5GNmzpwJsViMjh07okWLFjCZTNixYwcOHz6MLl26YMKECRg3bhxefvnlO/qcCRMmYPjw4Rg5ciTuu+8+FBUV2Y1OAMD48ePRrl07dO/eHS1atEBWVhaCg4Oxc+dOXL9+Hffccw+eeOIJPPjgg1izZs0dxRMcHIwDBw6gVatWGD58ODp06ID09HRUVlbaRm5qi6e+1zVUWFgYPvnkE/Tr1w8dOnTA22+/jU2bNiExMRGA479RXl6e0z9PiUSCTZs24ccff0SXLl2wfPlyLF682O51FosFkydPRocOHfDwww+jXbt2ePPNN5363oiaKpFw88QvERERkQ/iCA8RERH5PCY8REQNdPDgQSiVylofRNT4cEqLiKiBKisr8fPPP9d6XqPReDAaIrodTHiIiIjI53FKi4iIiHweEx4iIiLyeUx4iIiIyOcx4SEiIiKfx4SHiIiIfB4THiIiIvJ5THiIiIjI5/0/xjgFLfYo/44AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=tstatus, y= trating)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8c7df731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(rt['tomatometer_rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9f1c3ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9724194994574669"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(rt['tomatometer_rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e97ed49f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0958802609960896"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(rt['tomatometer_status'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "9146cbcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tomatometer_rating', ylabel='Count'>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=rt['tomatometer_rating'], kde =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "63d9cd15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='tomatometer_status', ylabel='Count'>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=rt['tomatometer_status'], kde =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e6375224",
   "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>tomatometer_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tomatometer_rating\n",
       "0                49.0\n",
       "1                87.0\n",
       "2                67.0\n",
       "3               100.0\n",
       "4                89.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tomratingdf=pd.DataFrame(rt['tomatometer_rating'])\n",
    "tomstatusdf=pd.DataFrame(rt['tomatometer_status'])\n",
    "tomratingdf.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "891efe6a",
   "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>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tomatometer_status\n",
       "0                   2\n",
       "1                   0\n",
       "2                   1\n",
       "3                   0\n",
       "4                   1"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tomstatusdf.head()"
   ]
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   "cell_type": "code",
   "execution_count": 121,
   "id": "69d90e21",
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    "model=LinearRegression()"
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  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "40b66210",
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      ],
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       "LinearRegression()"
      ]
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     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "model.fit(tomratingdf,tomstatusdf)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "981348c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sneha\\Anaconda\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2.10700358]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(pd.DataFrame([20])) \n",
    "# 0 -  Certified Fresh\n",
    "# 1 - Fresh\n",
    "# 2 - Rotten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6d204abe",
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "0.6519493801980516"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "model.score(tomratingdf,tomstatusdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "520f3110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02111511]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "327127d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.5293057])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
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