3.8_HW_stat_case_study.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "769636cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats as st\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from gensim import corpora\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58963325",
   "metadata": {},
   "source": [
    "## Задание 1\n",
    "\n",
    "Ответьте на следующие вопросы:\n",
    "\n",
    "- Как критики относятся к спортивным играм?\n",
    "\n",
    "- Критикам нравятся больше игры на PC или на PS4?\n",
    "\n",
    "- Критикам больше нравятся стрелялки или стратегии?\n",
    "\n",
    "Для каждого вопроса:\n",
    "\n",
    "- сформулируйте нулевую и альтернативную гипотезы;\n",
    "- выберите пороговый уровень статистической значимости;\n",
    "- опишите полученные результаты статистического теста."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6448fcc1",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>82.53</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>35.52</td>\n",
       "      <td>82.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>709.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>32.77</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>16714</th>\n",
       "      <td>Samurai Warriors: Sanada Maru</td>\n",
       "      <td>PS3</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Action</td>\n",
       "      <td>Tecmo Koei</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16715</th>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>X360</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Codemasters</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16716</th>\n",
       "      <td>Haitaka no Psychedelica</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Idea Factory</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16717</th>\n",
       "      <td>Spirits &amp; Spells</td>\n",
       "      <td>GBA</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Wanadoo</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>Winning Post 8 2016</td>\n",
       "      <td>PSV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>Simulation</td>\n",
       "      <td>Tecmo Koei</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Name Platform  Year_of_Release         Genre  \\\n",
       "0                         Wii Sports      Wii           2006.0        Sports   \n",
       "1                  Super Mario Bros.      NES           1985.0      Platform   \n",
       "2                     Mario Kart Wii      Wii           2008.0        Racing   \n",
       "3                  Wii Sports Resort      Wii           2009.0        Sports   \n",
       "4           Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing   \n",
       "...                              ...      ...              ...           ...   \n",
       "16714  Samurai Warriors: Sanada Maru      PS3           2016.0        Action   \n",
       "16715               LMA Manager 2007     X360           2006.0        Sports   \n",
       "16716        Haitaka no Psychedelica      PSV           2016.0     Adventure   \n",
       "16717               Spirits & Spells      GBA           2003.0      Platform   \n",
       "16718            Winning Post 8 2016      PSV           2016.0    Simulation   \n",
       "\n",
       "          Publisher  NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \\\n",
       "0          Nintendo     41.36     28.96      3.77         8.45         82.53   \n",
       "1          Nintendo     29.08      3.58      6.81         0.77         40.24   \n",
       "2          Nintendo     15.68     12.76      3.79         3.29         35.52   \n",
       "3          Nintendo     15.61     10.93      3.28         2.95         32.77   \n",
       "4          Nintendo     11.27      8.89     10.22         1.00         31.37   \n",
       "...             ...       ...       ...       ...          ...           ...   \n",
       "16714    Tecmo Koei      0.00      0.00      0.01         0.00          0.01   \n",
       "16715   Codemasters      0.00      0.01      0.00         0.00          0.01   \n",
       "16716  Idea Factory      0.00      0.00      0.01         0.00          0.01   \n",
       "16717       Wanadoo      0.01      0.00      0.00         0.00          0.01   \n",
       "16718    Tecmo Koei      0.00      0.00      0.01         0.00          0.01   \n",
       "\n",
       "       Critic_Score  Critic_Count User_Score  User_Count Developer Rating  \n",
       "0              76.0          51.0          8       322.0  Nintendo      E  \n",
       "1               NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "2              82.0          73.0        8.3       709.0  Nintendo      E  \n",
       "3              80.0          73.0          8       192.0  Nintendo      E  \n",
       "4               NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "...             ...           ...        ...         ...       ...    ...  \n",
       "16714           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16715           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16716           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16717           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "16718           NaN           NaN        NaN         NaN       NaN    NaN  \n",
       "\n",
       "[16719 rows x 16 columns]"
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   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/obulygin/pyda_homeworks/master/stat_case_study/vgsales.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357f7e14",
   "metadata": {},
   "source": [
    "### Как критики относятся к спортивным играм?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb366e5f",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2348.000000</td>\n",
       "      <td>1194.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>0.567291</td>\n",
       "      <td>71.968174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.084460</td>\n",
       "      <td>13.878433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.220000</td>\n",
       "      <td>75.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.552500</td>\n",
       "      <td>82.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
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       "      <td>98.000000</td>\n",
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      "text/plain": [
       "       Global_Sales  Critic_Score\n",
       "count   2348.000000   1194.000000\n",
       "mean       0.567291     71.968174\n",
       "std        2.084460     13.878433\n",
       "min        0.010000     19.000000\n",
       "25%        0.090000     64.000000\n",
       "50%        0.220000     75.000000\n",
       "75%        0.552500     82.000000\n",
       "max       82.530000     98.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "sport = df[['Name', 'Genre', 'Global_Sales', 'Critic_Score']]\n",
    "sport = sport[sport['Genre'] == 'Sports']\n",
    "\n",
    "sport.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "575cf2bc",
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   "outputs": [
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       "      <th>86</th>\n",
       "      <td>Mario &amp; Sonic at the Olympic Games</td>\n",
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       "      <td>7.99</td>\n",
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       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Zumba Fitness</td>\n",
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       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>FIFA 12</td>\n",
       "      <td>Sports</td>\n",
       "      <td>6.65</td>\n",
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       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Mario &amp; Sonic at the Olympic Games</td>\n",
       "      <td>Sports</td>\n",
       "      <td>5.09</td>\n",
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       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Mario &amp; Sonic at the Olympic Winter Games</td>\n",
       "      <td>Sports</td>\n",
       "      <td>4.53</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>16680</th>\n",
       "      <td>G1 Jockey 4 2008</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16692</th>\n",
       "      <td>Outdoors Unleashed: Africa 3D</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16697</th>\n",
       "      <td>PGA European Tour</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16704</th>\n",
       "      <td>Mezase!! Tsuri Master DS</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16715</th>\n",
       "      <td>LMA Manager 2007</td>\n",
       "      <td>Sports</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1154 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Name   Genre  Global_Sales  \\\n",
       "86            Mario & Sonic at the Olympic Games  Sports          7.99   \n",
       "119                                Zumba Fitness  Sports          6.71   \n",
       "122                                      FIFA 12  Sports          6.65   \n",
       "198           Mario & Sonic at the Olympic Games  Sports          5.09   \n",
       "237    Mario & Sonic at the Olympic Winter Games  Sports          4.53   \n",
       "...                                          ...     ...           ...   \n",
       "16680                           G1 Jockey 4 2008  Sports          0.01   \n",
       "16692              Outdoors Unleashed: Africa 3D  Sports          0.01   \n",
       "16697                          PGA European Tour  Sports          0.01   \n",
       "16704                   Mezase!! Tsuri Master DS  Sports          0.01   \n",
       "16715                           LMA Manager 2007  Sports          0.01   \n",
       "\n",
       "       Critic_Score  \n",
       "86              NaN  \n",
       "119             NaN  \n",
       "122             NaN  \n",
       "198             NaN  \n",
       "237             NaN  \n",
       "...             ...  \n",
       "16680           NaN  \n",
       "16692           NaN  \n",
       "16697           NaN  \n",
       "16704           NaN  \n",
       "16715           NaN  \n",
       "\n",
       "[1154 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sport[sport['Critic_Score'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d12be5f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.,  11.,  31.,  53.,  91., 153., 231., 345., 232.,  40.]),\n",
       " array([19. , 26.9, 34.8, 42.7, 50.6, 58.5, 66.4, 74.3, 82.2, 90.1, 98. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iiy8qGAyqX79+2rBhg6ZNmyZJOnfunHJzc7Vz506NHz/+937fUCgkj8ejYDCo9PT07m4fAHrMfa/s6O0twGJnVk7o7S1YoTu/v2/rNSrBYFCSlJGRIUlqbGxUIBBQWVmZs8btdmvEiBE6dOiQJKm+vl6XLl2KWOP3+1VYWOisuVY4HFYoFIo4AABA/Ot2qBhjNH/+fA0fPlyFhYWSpEAgIEnyer0Ra71er3MtEAgoOTlZffv2vemaa1VXV8vj8ThHbm5ud7cNAABiSLdDZd68efrkk0/0b//2b9ddc7lcEbeNMdedu9aXrVmyZImCwaBzNDU1dXfbAAAghnQrVCoqKrR9+3bt379f/fv3d877fD5Juu6RkZaWFudRFp/Pp87OTrW2tt50zbXcbrfS09MjDgAAEP+iChVjjObNm6ctW7Zo3759ys/Pj7ien58vn8+n2tpa51xnZ6fq6upUWloqSSouLlZSUlLEmubmZh0/ftxZAwAAIEmJ0SyeO3euNm3apB/96EdKS0tzHjnxeDxKSUmRy+VSZWWlVqxYoYKCAhUUFGjFihXq06ePpk+f7qydPXu2FixYoMzMTGVkZGjhwoUqKirS2LFje35CAAAQs6IKlTVr1kiSRo4cGXF+7dq1mjlzpiRp0aJF6ujo0EsvvaTW1lYNGTJEe/bsUVpamrN+9erVSkxM1NSpU9XR0aExY8Zo3bp1SkhIuL1pAABAXLmtz1HpLXyOCgDb8Dkq+DJ8jsrvfOWfowIAAHAnESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKwVdah8+OGHevLJJ+X3++VyubRt27aI6zNnzpTL5Yo4hg4dGrEmHA6roqJCWVlZSk1N1aRJk3T27NnbGgQAAMSfxGi/4OLFixo8eLD+6q/+SlOmTLnhmieeeEJr1651bicnJ0dcr6ys1I9//GPV1NQoMzNTCxYs0MSJE1VfX6+EhIRotwQgztz3yo7e3gIAS0QdKuXl5SovL//SNW63Wz6f74bXgsGg3nnnHW3YsEFjx46VJG3cuFG5ubnau3evxo8fH+2WAABAnLojr1E5cOCAsrOz9dBDD+mFF15QS0uLc62+vl6XLl1SWVmZc87v96uwsFCHDh264f2Fw2GFQqGIAwAAxL8eD5Xy8nK999572rdvn15//XUdOXJEo0ePVjgcliQFAgElJyerb9++EV/n9XoVCARueJ/V1dXyeDzOkZub29PbBgAAFor6qZ/fZ9q0ac6/CwsLVVJSory8PO3YsUOTJ0++6dcZY+RyuW54bcmSJZo/f75zOxQKESsAAHwN3PG3J+fk5CgvL0+nTp2SJPl8PnV2dqq1tTViXUtLi7xe7w3vw+12Kz09PeIAAADx746Hyvnz59XU1KScnBxJUnFxsZKSklRbW+usaW5u1vHjx1VaWnqntwMAAGJI1E/9tLe369NPP3VuNzY26tixY8rIyFBGRoaqqqo0ZcoU5eTk6MyZM1q6dKmysrL0zDPPSJI8Ho9mz56tBQsWKDMzUxkZGVq4cKGKioqcdwEBAABI3QiVo0ePatSoUc7tq68dmTFjhtasWaOGhgatX79eFy5cUE5OjkaNGqXNmzcrLS3N+ZrVq1crMTFRU6dOVUdHh8aMGaN169bxGSoAACCCyxhjensT0QqFQvJ4PAoGg7xeBYhDfOAb4s2ZlRN6ewtW6M7vb/7WDwAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWlGHyocffqgnn3xSfr9fLpdL27Zti7hujFFVVZX8fr9SUlI0cuRInThxImJNOBxWRUWFsrKylJqaqkmTJuns2bO3NQgAAIg/UYfKxYsXNXjwYL311ls3vP7aa69p1apVeuutt3TkyBH5fD6NGzdObW1tzprKykpt3bpVNTU1OnjwoNrb2zVx4kR1dXV1fxIAABB3EqP9gvLycpWXl9/wmjFGb7zxhpYtW6bJkydLkt599115vV5t2rRJL774ooLBoN555x1t2LBBY8eOlSRt3LhRubm52rt3r8aPH38b4wAAgHjSo69RaWxsVCAQUFlZmXPO7XZrxIgROnTokCSpvr5ely5diljj9/tVWFjorLlWOBxWKBSKOAAAQPzr0VAJBAKSJK/XG3He6/U61wKBgJKTk9W3b9+brrlWdXW1PB6Pc+Tm5vbktgEAgKXuyLt+XC5XxG1jzHXnrvVla5YsWaJgMOgcTU1NPbZXAABgrx4NFZ/PJ0nXPTLS0tLiPMri8/nU2dmp1tbWm665ltvtVnp6esQBAADiX4+GSn5+vnw+n2pra51znZ2dqqurU2lpqSSpuLhYSUlJEWuam5t1/PhxZw0AAIDUjXf9tLe369NPP3VuNzY26tixY8rIyNCAAQNUWVmpFStWqKCgQAUFBVqxYoX69Omj6dOnS5I8Ho9mz56tBQsWKDMzUxkZGVq4cKGKioqcdwEBAABI3QiVo0ePatSoUc7t+fPnS5JmzJihdevWadGiRero6NBLL72k1tZWDRkyRHv27FFaWprzNatXr1ZiYqKmTp2qjo4OjRkzRuvWrVNCQkIPjAQAAOKFyxhjensT0QqFQvJ4PAoGg7xeBYhD972yo7e3APSoMysn9PYWrNCd39/8rR8AAGAtQgUAAFgr6teoAIgtPI0CIJbxiAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALBWj4dKVVWVXC5XxOHz+ZzrxhhVVVXJ7/crJSVFI0eO1IkTJ3p6GwAAIA4k3ok7feSRR7R3717ndkJCgvPv1157TatWrdK6dev00EMP6dVXX9W4ceN08uRJpaWl3YntAD3mvld29PYWAOBr5Y489ZOYmCifz+cc/fr1k/S7R1PeeOMNLVu2TJMnT1ZhYaHeffddff7559q0adOd2AoAAIhhdyRUTp06Jb/fr/z8fD377LM6ffq0JKmxsVGBQEBlZWXOWrfbrREjRujQoUM3vb9wOKxQKBRxAACA+NfjoTJkyBCtX79eu3fv1ttvv61AIKDS0lKdP39egUBAkuT1eiO+xuv1OtdupLq6Wh6Pxzlyc3N7etsAAMBCPR4q5eXlmjJlioqKijR27Fjt2PG75/TfffddZ43L5Yr4GmPMdee+aMmSJQoGg87R1NTU09sGAAAWuuNvT05NTVVRUZFOnTrlvPvn2kdPWlparnuU5YvcbrfS09MjDgAAEP/ueKiEw2H9/Oc/V05OjvLz8+Xz+VRbW+tc7+zsVF1dnUpLS+/0VgAAQIzp8bcnL1y4UE8++aQGDBiglpYWvfrqqwqFQpoxY4ZcLpcqKyu1YsUKFRQUqKCgQCtWrFCfPn00ffr0nt4KAACIcT0eKmfPntVzzz2n3/zmN+rXr5+GDh2qw4cPKy8vT5K0aNEidXR06KWXXlJra6uGDBmiPXv28BkqAADgOi5jjOntTUQrFArJ4/EoGAzyehV8pfjANwDdcWblhN7eghW68/ubv/UDAACsRagAAABrESoAAMBad+SPEgIAgP8Xi69vs+V1NTyiAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKzFJ9Oi18TiJzUCAL5aPKICAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqJvb0B9Iz7XtnR21sAAKDH8YgKAACwFqECAACsRagAAABr8RqVG+D1HgAA2IFHVAAAgLUIFQAAYK1eDZV/+qd/Un5+vu6++24VFxfrP//zP3tzOwAAwDK9FiqbN29WZWWlli1bpo8//lh/+qd/qvLycn322We9tSUAAGCZXguVVatWafbs2fr2t7+tb37zm3rjjTeUm5urNWvW9NaWAACAZXrlXT+dnZ2qr6/XK6+8EnG+rKxMhw4dum59OBxWOBx2bgeDQUlSKBS6I/u7Ev78jtwvAACx4k78jr16n8aYW/6aXgmV3/zmN+rq6pLX64047/V6FQgErltfXV2tv//7v7/ufG5u7h3bIwAAX2eeN+7cfbe1tcnj8dzS2l79HBWXyxVx2xhz3TlJWrJkiebPn+/cvnLlin77298qMzPzhuvvhFAopNzcXDU1NSk9Pf0r+Z5fNWaMD8wYH5gxPjBjJGOM2tra5Pf7b/n+eyVUsrKylJCQcN2jJy0tLdc9yiJJbrdbbrc74twf/MEf3Mkt3lR6enrc/sd2FTPGB2aMD8wYH5jx/93qIylX9cqLaZOTk1VcXKza2tqI87W1tSotLe2NLQEAAAv12lM/8+fP1/PPP6+SkhINGzZMP/zhD/XZZ59pzpw5vbUlAABgmV4LlWnTpun8+fP67ne/q+bmZhUWFmrnzp3Ky8vrrS19KbfbreXLl1/3FFQ8Ycb4wIzxgRnjAzPePpeJ5j1CAAAAXyH+1g8AALAWoQIAAKxFqAAAAGsRKgAAwFqEyhdUV1frscceU1pamrKzs/X000/r5MmTEWuMMaqqqpLf71dKSopGjhypEydO9NKOo7dmzRoNGjTI+WCeYcOG6YMPPnCux/p8N1JdXS2Xy6XKykrnXKzPWVVVJZfLFXH4fD7neqzPd9WvfvUrfetb31JmZqb69OmjP/zDP1R9fb1zPdbnvO+++677ObpcLs2dO1dS7M8nSZcvX9bf/d3fKT8/XykpKbr//vv13e9+V1euXHHWxMOcbW1tqqysVF5enlJSUlRaWqojR44412Ntxg8//FBPPvmk/H6/XC6Xtm3bFnH9VuYJh8OqqKhQVlaWUlNTNWnSJJ09ezb6zRg4xo8fb9auXWuOHz9ujh07ZiZMmGAGDBhg2tvbnTUrV640aWlp5v333zcNDQ1m2rRpJicnx4RCoV7c+a3bvn272bFjhzl58qQ5efKkWbp0qUlKSjLHjx83xsT+fNf62c9+Zu677z4zaNAg8/LLLzvnY33O5cuXm0ceecQ0Nzc7R0tLi3M91uczxpjf/va3Ji8vz8ycOdP89Kc/NY2NjWbv3r3m008/ddbE+pwtLS0RP8Pa2lojyezfv98YE/vzGWPMq6++ajIzM81PfvIT09jYaP793//d3HPPPeaNN95w1sTDnFOnTjUDBw40dXV15tSpU2b58uUmPT3dnD171hgTezPu3LnTLFu2zLz//vtGktm6dWvE9VuZZ86cOebee+81tbW15qOPPjKjRo0ygwcPNpcvX45qL4TKl2hpaTGSTF1dnTHGmCtXrhifz2dWrlzprPnf//1f4/F4zD//8z/31jZvW9++fc2//uu/xt18bW1tpqCgwNTW1poRI0Y4oRIPcy5fvtwMHjz4htfiYT5jjFm8eLEZPnz4Ta/Hy5xf9PLLL5sHHnjAXLlyJW7mmzBhgpk1a1bEucmTJ5tvfetbxpj4+Dl+/vnnJiEhwfzkJz+JOD948GCzbNmymJ/x2lC5lXkuXLhgkpKSTE1NjbPmV7/6lbnrrrvMrl27ovr+PPXzJYLBoCQpIyNDktTY2KhAIKCysjJnjdvt1ogRI3To0KFe2ePt6OrqUk1NjS5evKhhw4bF3Xxz587VhAkTNHbs2Ijz8TLnqVOn5Pf7lZ+fr2effVanT5+WFD/zbd++XSUlJfrzP/9zZWdn69FHH9Xbb7/tXI+XOa/q7OzUxo0bNWvWLLlcrriZb/jw4fqP//gP/eIXv5Ak/dd//ZcOHjyoP/uzP5MUHz/Hy5cvq6urS3fffXfE+ZSUFB08eDAuZvyiW5mnvr5ely5diljj9/tVWFgY9cyEyk0YYzR//nwNHz5chYWFkuT8EcVr/3Ci1+u97g8s2qyhoUH33HOP3G635syZo61bt2rgwIFxM58k1dTUqL6+XtXV1dddi4c5hwwZovXr12v37t16++23FQgEVFpaqvPnz8fFfJJ0+vRprVmzRgUFBdq9e7fmzJmjv/7rv9b69eslxcfP8Yu2bdumCxcuaObMmZLiZ77Fixfrueee0ze+8Q0lJSXp0UcfVWVlpZ577jlJ8TFnWlqahg0bpu9973s6d+6curq6tHHjRv30pz9Vc3NzXMz4RbcyTyAQUHJysvr27XvTNbeq1z5C33bz5s3TJ598ooMHD153zeVyRdw2xlx3zmYPP/ywjh07pgsXLuj999/XjBkzVFdX51yP9fmampr08ssva8+ePdf9P5wviuU5y8vLnX8XFRVp2LBheuCBB/Tuu+9q6NChkmJ7Pkm6cuWKSkpKtGLFCknSo48+qhMnTmjNmjX6y7/8S2ddrM951TvvvKPy8nL5/f6I87E+3+bNm7Vx40Zt2rRJjzzyiI4dO6bKykr5/X7NmDHDWRfrc27YsEGzZs3Svffeq4SEBP3RH/2Rpk+fro8++shZE+szXqs783RnZh5RuYGKigpt375d+/fvV//+/Z3zV99VcW0NtrS0XFeWNktOTtaDDz6okpISVVdXa/DgwXrzzTfjZr76+nq1tLSouLhYiYmJSkxMVF1dnf7xH/9RiYmJziyxPucXpaamqqioSKdOnYqbn2NOTo4GDhwYce6b3/ymPvvsM0nx879HSfrlL3+pvXv36tvf/rZzLl7m+9u//Vu98sorevbZZ1VUVKTnn39ef/M3f+M82hkvcz7wwAOqq6tTe3u7mpqa9LOf/UyXLl1Sfn5+3Mx41a3M4/P51NnZqdbW1puuuVWEyhcYYzRv3jxt2bJF+/btU35+fsT1q//B1dbWOuc6OztVV1en0tLSr3q7PcYYo3A4HDfzjRkzRg0NDTp27JhzlJSU6C/+4i907Ngx3X///XEx5xeFw2H9/Oc/V05OTtz8HP/kT/7kuo8H+MUvfuH84dJ4mVOS1q5dq+zsbE2YMME5Fy/zff7557rrrshfNQkJCc7bk+NlzqtSU1OVk5Oj1tZW7d69W0899VTczXgr8xQXFyspKSliTXNzs44fPx79zFG//DeOfec73zEej8ccOHAg4i2Dn3/+ubNm5cqVxuPxmC1btpiGhgbz3HPPWf0Ws2stWbLEfPjhh6axsdF88sknZunSpeauu+4ye/bsMcbE/nw388V3/RgT+3MuWLDAHDhwwJw+fdocPnzYTJw40aSlpZkzZ84YY2J/PmN+99byxMRE8w//8A/m1KlT5r333jN9+vQxGzdudNbEw5xdXV1mwIABZvHixdddi4f5ZsyYYe69917n7clbtmwxWVlZZtGiRc6aeJhz165d5oMPPjCnT582e/bsMYMHDzZ//Md/bDo7O40xsTdjW1ub+fjjj83HH39sJJlVq1aZjz/+2Pzyl780xtzaPHPmzDH9+/c3e/fuNR999JEZPXo0b0++XZJueKxdu9ZZc+XKFbN8+XLj8/mM2+02jz/+uGloaOi9TUdp1qxZJi8vzyQnJ5t+/fqZMWPGOJFiTOzPdzPXhkqsz3n1MwuSkpKM3+83kydPNidOnHCux/p8V/34xz82hYWFxu12m2984xvmhz/8YcT1eJhz9+7dRpI5efLkddfiYb5QKGRefvllM2DAAHP33Xeb+++/3yxbtsyEw2FnTTzMuXnzZnP//feb5ORk4/P5zNy5c82FCxec67E24/79+2/4+3DGjBnGmFubp6Ojw8ybN89kZGSYlJQUM3HiRPPZZ59FvReXMcZ09+EfAACAO4nXqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKz1f+nT/HGu2CjnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sport['Critic_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fc465a36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Не отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "result = st.ttest_1samp(sport['Critic_Score'], 70, alternative='less')\n",
    "\n",
    "if result.pvalue < alpha:\n",
    "    print('Отвергаем нулевую гипотезу, среднее меньше 70 баллов')\n",
    "else:\n",
    "    print('Не отвергаем нулевую гипотезу')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba93b42",
   "metadata": {},
   "source": [
    "#### Критикам нравятся больше игры на PC или на PS4?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b150d291",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1367 entries, 31 to 16709\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Name          1367 non-null   object \n",
      " 1   Platform      1367 non-null   object \n",
      " 2   Global_Sales  1367 non-null   float64\n",
      " 3   Critic_Score  967 non-null    float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 53.4+ KB\n"
     ]
    }
   ],
   "source": [
    "platform = df[['Name', 'Platform', 'Global_Sales', 'Critic_Score']]\n",
    "platform = platform[(platform['Platform'] == 'PC') | (platform['Platform'] == 'PS4')]\n",
    "\n",
    "platform.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20139d53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  2.,   3.,   8.,  24.,  40., 105., 194., 289., 234.,  68.]),\n",
       " array([19. , 26.8, 34.6, 42.4, 50.2, 58. , 65.8, 73.6, 81.4, 89.2, 97. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(platform['Critic_Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0030c765",
   "metadata": {},
   "outputs": [],
   "source": [
    "pl_PC = platform[platform['Platform'] == 'PC']\n",
    "pl_PS4 = platform[platform['Platform'] == 'PS4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d0566e18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Не отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "result = st.ttest_ind(pl_PC['Critic_Score'], pl_PS4['Critic_Score'], equal_var=False)\n",
    "\n",
    "if result.pvalue < alpha:\n",
    "    print('Отвергаем нулевую гипотезу, удовлетворенность критиков играми на PC & PS4 разная')\n",
    "else:\n",
    "    print('Не отвергаем нулевую гипотезу')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b876abf",
   "metadata": {},
   "source": [
    "Вывод: так как мы не отвергаем H0, то можем сказать, что игроки в целом одинаково удовлетворены играми на PC и играми на PS4. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8eca8d2",
   "metadata": {},
   "source": [
    "### Критикам больше нравятся стрелялки или стратегии?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e8266c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sports', 'Platform', 'Racing', 'Role-Playing', 'Puzzle', 'Misc',\n",
       "       'Shooter', 'Simulation', 'Action', 'Fighting', 'Adventure',\n",
       "       'Strategy', nan], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Genre'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5b6dbce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1323 entries, 9 to 16710\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Name          1323 non-null   object \n",
      " 1   Genre         1323 non-null   object \n",
      " 2   Global_Sales  1323 non-null   float64\n",
      " 3   Critic_Score  944 non-null    float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 51.7+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 683 entries, 168 to 16706\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Name          683 non-null    object \n",
      " 1   Genre         683 non-null    object \n",
      " 2   Global_Sales  683 non-null    float64\n",
      " 3   Critic_Score  302 non-null    float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 26.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huinya = df[['Name', 'Genre', 'Global_Sales', 'Critic_Score']]\n",
    "shoot = huinya[huinya['Genre'] == 'Shooter']\n",
    "strat = huinya[huinya['Genre'] == 'Strategy']\n",
    "\n",
    "shoot.info(), strat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f932a7e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Не отвергаем нулевую гипотезу\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "result = st.ttest_ind(shoot['Critic_Score'], strat['Critic_Score'], equal_var=False)\n",
    "\n",
    "if result.pvalue < alpha:\n",
    "    print('Отвергаем нулевую гипотезу, критики по-разному оценивают стрелялки и стратегии')\n",
    "else:\n",
    "    print('Не отвергаем нулевую гипотезу')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6566ef13",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shali\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shali\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Critic_Score'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(shoot['Critic_Score'])\n",
    "plt.show()\n",
    "sns.boxplot(strat['Critic_Score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0befb47",
   "metadata": {},
   "source": [
    "Оценки критиков шутеров и стратегий не сильно различаются"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fdde7a",
   "metadata": {},
   "source": [
    "## Задание 2\n",
    "\n",
    "Реализуйте базовую модель логистической регрессии для классификации текстовых сообщений (используемые данные здесь) по признаку спама. Для этого:\n",
    "\n",
    "- Привидите весь текст к нижнему регистру;\n",
    "- Удалите мусорные символы;\n",
    "- Удалите стоп-слова;\n",
    "- Привидите все слова к начальной форме;\n",
    "- Преобразуйте все сообщения в вектора TF-IDF. Можете поэкспериментировать с параметрами TfidfVectorizer;\n",
    "- Разделите данные на тестовые и тренировочные в соотношении 30/70, укажите random_state=42. Используйте train_test_split;\n",
    "- Постройте модель логистической регрессии, укажите random_state=42, оцените ее точность на тестовых данных;\n",
    "- Опишите результаты при помощи confusion_matrix;\n",
    "- Постройте датафрейм, который будет содержать все исходные тексты сообщений, классифицированные неправильно (с указанием фактического и предсказанного)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b259cf4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam = pd.read_csv('https://raw.githubusercontent.com/obulygin/pyda_homeworks/master/stat_case_study/spam.csv')\n",
    "spam.loc[spam.Category == 'ham', 'Class'] = 1\n",
    "spam.loc[spam.Category == 'spam', 'Class'] = 0\n",
    "\n",
    "spam['Class'] = spam['Class'].astype('int')\n",
    "spam = spam.drop(['Category'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e8862dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam_class = spam['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "547e14f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam['Message'] = spam['Message'].str.lower()\n",
    "\n",
    "spam['Message'] = spam['Message'].replace(['[\\W_]+'], [' '], regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "42ae4dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\shali\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\stopwords.zip.\n"
     ]
    }
   ],
   "source": [
    "spam['Message'] = spam['Message'].str[1:-1].str.split(' ').tolist()\n",
    "\n",
    "nltk.download('stopwords')\n",
    "stopwords_set = set(stopwords.words('english'))\n",
    "spam['Message'] = pd.Series([[word for word in text if word not in stopwords_set] for text in spam['Message']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d7d0c4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\shali\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\shali\\AppData\\Roaming\\nltk_data...\n"
     ]
    }
   ],
   "source": [
    "nltk.download('omw-1.4')\n",
    "nltk.download('wordnet')\n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "spam['Message'] = pd.Series([wordnet_lemmatizer.lemmatize(word) for word in text] for text in spam['Message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68af2fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam['Message'] = spam['Message'].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7f0eda33",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(stop_words=list(stopwords_set))\n",
    "tfidf_matrix = tfidf.fit_transform(spam['Message'])\n",
    "names = tfidf.get_feature_names_out()\n",
    "tfidf_matrix = pd.DataFrame(tfidf_matrix.toarray(), columns=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1084c51b",
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       "[5572 rows x 8799 columns]"
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     "execution_count": 36,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb3a69aa",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.387454</td>\n",
       "      <td>0.679902</td>\n",
       "      <td>0.242555</td>\n",
       "      <td>0.230066</td>\n",
       "      <td>0.288804</td>\n",
       "      <td>0.263717</td>\n",
       "      <td>0.335423</td>\n",
       "      <td>0.334502</td>\n",
       "      <td>0.233708</td>\n",
       "      <td>0.565823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.409377</td>\n",
       "      <td>0.310791</td>\n",
       "      <td>0.325416</td>\n",
       "      <td>0.323964</td>\n",
       "      <td>0.415844</td>\n",
       "      <td>0.314143</td>\n",
       "      <td>0.282794</td>\n",
       "      <td>0.162708</td>\n",
       "      <td>0.233741</td>\n",
       "      <td>0.350773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 8799 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                00          000       000pes          007  008704050406  \\\n",
       "count  5572.000000  5572.000000  5572.000000  5572.000000   5572.000000   \n",
       "mean      0.000622     0.001292     0.000044     0.000082      0.000100   \n",
       "std       0.012670     0.019421     0.003249     0.004353      0.005282   \n",
       "min       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "max       0.387454     0.679902     0.242555     0.230066      0.288804   \n",
       "\n",
       "              0089         0121  01223585236  01223585334   0125698789  ...  \\\n",
       "count  5572.000000  5572.000000  5572.000000  5572.000000  5572.000000  ...   \n",
       "mean      0.000047     0.000060     0.000060     0.000084     0.000102  ...   \n",
       "std       0.003533     0.004494     0.004481     0.004427     0.007580  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       0.263717     0.335423     0.334502     0.233708     0.565823  ...   \n",
       "\n",
       "              zero        zhong       zindgi          zoe    zogtorius  \\\n",
       "count  5572.000000  5572.000000  5572.000000  5572.000000  5572.000000   \n",
       "mean      0.000073     0.000056     0.000058     0.000058     0.000075   \n",
       "std       0.005484     0.004164     0.004359     0.004340     0.005571   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       0.409377     0.310791     0.325416     0.323964     0.415844   \n",
       "\n",
       "              zoom         zouk        zyada           ú1          〨ud  \n",
       "count  5572.000000  5572.000000  5572.000000  5572.000000  5572.000000  \n",
       "mean      0.000056     0.000051     0.000029     0.000042     0.000063  \n",
       "std       0.004208     0.003788     0.002180     0.003131     0.004699  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       0.314143     0.282794     0.162708     0.233741     0.350773  \n",
       "\n",
       "[8 rows x 8799 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_matrix.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2330cc38",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(tfidf_matrix, spam_class, random_state = 42, test_size = 0.30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2266956d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9527511961722488"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state=42).fit(x_train, y_train)\n",
    "y_pred = lr.predict(x_test)\n",
    "lr.predict_proba(x_test)\n",
    "\n",
    "lr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "73853667",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 150,   74],\n",
       "       [   5, 1443]], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, pd.Series(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f9eeb86b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reset_index()\n",
    "\n",
    "result = pd.DataFrame([y_test['Class'], y_pred]).T\n",
    "result.rename(columns={'Class': 'fact', 'Unnamed 0': 'pred'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f4cb1818",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fact</th>\n",
       "      <th>pred</th>\n",
       "      <th>texts</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>jurong point crazy available bugis n great wor...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>k lar joking wif u oni</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ree entry 2 wkly comp win fa cup final tkts 21...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>dun say early hor u c already say</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ah think go usf life around thoug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>uess spent last night phasing fourth dimensio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>dad gonna call get work ask crazy question</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>e said</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ery hurting n meaningful line ever compromised...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>mao nice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1672 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fact  pred                                              texts\n",
       "0        1     1  jurong point crazy available bugis n great wor...\n",
       "1        1     1                             k lar joking wif u oni\n",
       "2        1     1  ree entry 2 wkly comp win fa cup final tkts 21...\n",
       "3        1     1                  dun say early hor u c already say\n",
       "4        1     1                  ah think go usf life around thoug\n",
       "...    ...   ...                                                ...\n",
       "1667     1     1      uess spent last night phasing fourth dimensio\n",
       "1668     0     0         dad gonna call get work ask crazy question\n",
       "1669     1     1                                             e said\n",
       "1670     0     0  ery hurting n meaningful line ever compromised...\n",
       "1671     1     1                                          mao nice \n",
       "\n",
       "[1672 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['texts'] = spam['Message']\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "337f40c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fact</th>\n",
       "      <th>pred</th>\n",
       "      <th>texts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>h u remember 2 spell name yes v naughty make v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>l go ahead watt wanted sure great weekend abiol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>air enough anything going</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>call</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>next stop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1525</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ct employee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>oday accept day u accept brother sister lover ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>ounds gd haha wah u yan jiu fast liao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1586</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>wondering would okay call uncle john let know...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fact  pred                                              texts\n",
       "17       1     0  h u remember 2 spell name yes v naughty make v...\n",
       "40       0     1    l go ahead watt wanted sure great weekend abiol\n",
       "47       0     1                          air enough anything going\n",
       "74       0     1                                               call\n",
       "84       0     1                                          next stop\n",
       "...    ...   ...                                                ...\n",
       "1525     0     1                                        ct employee\n",
       "1567     0     1                                               late\n",
       "1569     0     1  oday accept day u accept brother sister lover ...\n",
       "1576     1     0              ounds gd haha wah u yan jiu fast liao\n",
       "1586     0     1   wondering would okay call uncle john let know...\n",
       "\n",
       "[79 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[result.fact != result.pred]\n"
   ]
  }
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