alex-coch2-baseline-sf-tripadvisor-rating-v2-7.ipynb

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    "![](https://www.pata.org/wp-content/uploads/2014/09/TripAdvisor_Logo-300x119.png)\n",
    "# Predict TripAdvisor Rating\n",
    "## В этом соревновании нам предстоит предсказать рейтинг ресторана в TripAdvisor\n",
    "**По ходу задачи:**\n",
    "* Прокачаем работу с pandas\n",
    "* Научимся работать с Kaggle Notebooks\n",
    "* Поймем как делать предобработку различных данных\n",
    "* Научимся работать с пропущенными данными (Nan)\n",
    "* Познакомимся с различными видами кодирования признаков\n",
    "* Немного попробуем [Feature Engineering](https://ru.wikipedia.org/wiki/Конструирование_признаков) (генерировать новые признаки)\n",
    "* И совсем немного затронем ML\n",
    "* И многое другое...   \n",
    "\n",
    "\n",
    "\n",
    "### И самое важное, все это вы сможете сделать самостоятельно!\n",
    "\n",
    "*Этот Ноутбук являетсся Примером/Шаблоном к этому соревнованию (Baseline) и не служит готовым решением!*   \n",
    "Вы можете использовать его как основу для построения своего решения.\n",
    "\n",
    "> что такое baseline решение, зачем оно нужно и почему предоставлять baseline к соревнованию стало важным стандартом на kaggle и других площадках.   \n",
    "**baseline** создается больше как шаблон, где можно посмотреть как происходит обращение с входящими данными и что нужно получить на выходе. При этом МЛ начинка может быть достаточно простой, просто для примера. Это помогает быстрее приступить к самому МЛ, а не тратить ценное время на чисто инженерные задачи. \n",
    "Также baseline являеться хорошей опорной точкой по метрике. Если твое решение хуже baseline - ты явно делаешь что-то не то и стоит попробовать другой путь) \n",
    "\n",
    "В контексте нашего соревнования baseline идет с небольшими примерами того, что можно делать с данными, и с инструкцией, что делать дальше, чтобы улучшить результат.  Вообще готовым решением это сложно назвать, так как используются всего 2 самых простых признака (а остальные исключаются)."
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import"
   ]
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    {
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     "text": [
      "/kaggle/input/sf-dst-restaurant-rating/sample_submission.csv\n",
      "/kaggle/input/sf-dst-restaurant-rating/main_task.csv\n",
      "/kaggle/input/sf-dst-restaurant-rating/kaggle_task.csv\n",
      "/kaggle/input/data-for-prediction-restaurant-rating/parsed.csv\n",
      "/kaggle/input/data-for-prediction-restaurant-rating/all_in.csv\n",
      "/kaggle/input/data-for-prediction-restaurant-rating/parser.py\n",
      "/kaggle/input/data-for-prediction-restaurant-rating/preprocessing.ipynb\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "%matplotlib inline\n",
    "\n",
    "# Загружаем специальный удобный инструмент для разделения датасета:\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "from sklearn import metrics \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
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   "source": [
    "# всегда фиксируйте RANDOM_SEED, чтобы ваши эксперименты были воспроизводимы!\n",
    "RANDOM_SEED = 42"
   ]
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   "execution_count": 3,
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   "source": [
    "# зафиксируем версию пакетов, чтобы эксперименты были воспроизводимы:\n",
    "!pip freeze > requirements.txt"
   ]
  },
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Restaurant_id</th>\n",
       "      <th>City</th>\n",
       "      <th>Cuisine Style</th>\n",
       "      <th>Ranking</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Price Range</th>\n",
       "      <th>Number of Reviews</th>\n",
       "      <th>ID_TA</th>\n",
       "      <th>Main</th>\n",
       "      <th>alive</th>\n",
       "      <th>...</th>\n",
       "      <th>rest_number</th>\n",
       "      <th>is_capital</th>\n",
       "      <th>cuisines_number</th>\n",
       "      <th>population</th>\n",
       "      <th>city_square</th>\n",
       "      <th>net_rest</th>\n",
       "      <th>negative</th>\n",
       "      <th>neutra</th>\n",
       "      <th>positive</th>\n",
       "      <th>compound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29867</th>\n",
       "      <td>id_1183</td>\n",
       "      <td>Hamburg</td>\n",
       "      <td>['Indian']</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$$ - $$$</td>\n",
       "      <td>22.0</td>\n",
       "      <td>d799935</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2915.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1718187</td>\n",
       "      <td>755.09</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.427</td>\n",
       "      <td>0.7874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41120</th>\n",
       "      <td>id_1105</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>['usual']</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$*</td>\n",
       "      <td>2.0</td>\n",
       "      <td>d12437603</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4690.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>547733</td>\n",
       "      <td>100.05</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.4215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>2 rows × 29 columns</p>\n",
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       "      Restaurant_id     City Cuisine Style  Ranking  Rating Price Range  \\\n",
       "29867       id_1183  Hamburg    ['Indian']   1185.0     4.0    $$ - $$$   \n",
       "41120       id_1105   Lisbon     ['usual']   2500.0     NaN          $*   \n",
       "\n",
       "       Number of Reviews      ID_TA   Main  alive  ...  rest_number  \\\n",
       "29867               22.0    d799935   True      0  ...       2915.0   \n",
       "41120                2.0  d12437603  False      1  ...       4690.0   \n",
       "\n",
       "       is_capital  cuisines_number  population  city_square  net_rest  \\\n",
       "29867           0                1     1718187       755.09         1   \n",
       "41120           1                1      547733       100.05         1   \n",
       "\n",
       "       negative  neutra positive  compound  \n",
       "29867       0.0   0.573    0.427    0.7874  \n",
       "41120       0.0   0.741    0.259    0.4215  \n",
       "\n",
       "[2 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Загрузим итоговые данные и проведем EDA (Разведочный анализ данных)\n",
    "df = pd.read_csv('/kaggle/input/data-for-prediction-restaurant-rating/all_in.csv')\n",
    "df.sample(2)"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 29 columns):\n",
      "Restaurant_id        50000 non-null object\n",
      "City                 50000 non-null object\n",
      "Cuisine Style        50000 non-null object\n",
      "Ranking              50000 non-null float64\n",
      "Rating               40000 non-null float64\n",
      "Price Range          50000 non-null object\n",
      "Number of Reviews    50000 non-null float64\n",
      "ID_TA                50000 non-null object\n",
      "Main                 50000 non-null bool\n",
      "alive                50000 non-null int64\n",
      "rating               50000 non-null float64\n",
      "excelent             50000 non-null float64\n",
      "very_good            50000 non-null float64\n",
      "average              50000 non-null float64\n",
      "poor                 50000 non-null float64\n",
      "terrible             50000 non-null float64\n",
      "time_between         50000 non-null float64\n",
      "last_time_review     50000 non-null float64\n",
      "text_reviews         41886 non-null object\n",
      "rest_number          50000 non-null float64\n",
      "is_capital           50000 non-null int64\n",
      "cuisines_number      50000 non-null int64\n",
      "population           50000 non-null int64\n",
      "city_square          50000 non-null float64\n",
      "net_rest             50000 non-null int64\n",
      "negative             50000 non-null float64\n",
      "neutra               50000 non-null float64\n",
      "positive             50000 non-null float64\n",
      "compound             50000 non-null float64\n",
      "dtypes: bool(1), float64(17), int64(5), object(6)\n",
      "memory usage: 10.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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      "count    50000.000000\n",
      "mean       116.720620\n",
      "std        286.112464\n",
      "min          0.000000\n",
      "25%          7.000000\n",
      "50%         28.000000\n",
      "75%        105.000000\n",
      "max       9660.000000\n",
      "Name: Number of Reviews, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdec11e1f98>"
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Разобьем признаки на категориальные, числовые и бинарные. (некоторые - условно, их нужно еще доработать)\n",
    "num_features = ['Number of Reviews', 'time_between', 'last_time_review', \\\n",
    "                'cuisines_number', 'population', 'city_square', 'Ranking', 'rating', 'rest_number',\\\n",
    "                'excelent', 'very_good', 'average', 'poor', 'terrible', \\\n",
    "                'negative', 'neutra', 'positive', 'compound']\n",
    "\n",
    "cat_features = ['City', 'Cuisine Style', 'Price Range', 'text_reviews']\n",
    "bin_features = ['alive', 'is_capital', 'net_rest']\n",
    "\n",
    "other_features = ['Restaurant_id', 'ID_TA', 'Main', ]\n",
    "# Числовые признаки\n",
    "# 'Number of Reviews'\n",
    "# Кол-во отзывов о ресторане\n",
    "print(df['Number of Reviews'].describe())\n",
    "df[(df['Number of Reviews'] > 100) & (df['Number of Reviews'] < 1000)]['Number of Reviews'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:48.720686Z",
     "iopub.status.busy": "2022-08-01T10:21:48.720008Z",
     "iopub.status.idle": "2022-08-01T10:21:49.296296Z",
     "shell.execute_reply": "2022-08-01T10:21:49.295132Z",
     "shell.execute_reply.started": "2022-08-01T10:21:48.720615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdeab5942e8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Много ресторанов с маленьким количеством отзывов и наоборот мало с большим.\n",
    "# Чтобы сгладить применим log\n",
    "df['Number of Reviews'] = df['Number of Reviews'].apply(lambda x: np.log(x) if x != 0 else x)\n",
    "df['Number of Reviews'].hist(bins=20)\n",
    "# стало намного симпатичнее и ближе к норм распределению"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:49.299325Z",
     "iopub.status.busy": "2022-08-01T10:21:49.298647Z",
     "iopub.status.idle": "2022-08-01T10:21:49.652292Z",
     "shell.execute_reply": "2022-08-01T10:21:49.650449Z",
     "shell.execute_reply.started": "2022-08-01T10:21:49.299239Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    50000.000000\n",
      "mean      2027.352160\n",
      "std       5345.107678\n",
      "min          0.000000\n",
      "25%          7.000000\n",
      "50%         59.000000\n",
      "75%        223.000000\n",
      "max      17585.000000\n",
      "Name: time_between, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea8ea9d30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'time_between'\n",
    "# время между отзывами\n",
    "print(df['time_between'].describe())\n",
    "#df[df['time_between'] < 0]['time_between'].hist(bins=20)\n",
    "df['time_between'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:49.655725Z",
     "iopub.status.busy": "2022-08-01T10:21:49.655023Z",
     "iopub.status.idle": "2022-08-01T10:21:50.103229Z",
     "shell.execute_reply": "2022-08-01T10:21:50.101645Z",
     "shell.execute_reply.started": "2022-08-01T10:21:49.655440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea8e1d470>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# сгладим этот некрасивый график логарифмом\n",
    "df['time_between'] = df['time_between'].apply(lambda x: np.log(x) if x != 0 else x)\n",
    "df['time_between'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:50.106953Z",
     "iopub.status.busy": "2022-08-01T10:21:50.105793Z",
     "iopub.status.idle": "2022-08-01T10:21:50.495094Z",
     "shell.execute_reply": "2022-08-01T10:21:50.493847Z",
     "shell.execute_reply.started": "2022-08-01T10:21:50.106264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    50000.000000\n",
      "mean      4687.608640\n",
      "std       6383.689645\n",
      "min       1584.000000\n",
      "25%       1681.000000\n",
      "50%       1809.000000\n",
      "75%       2263.000000\n",
      "max      19172.000000\n",
      "Name: last_time_review, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea8d88438>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'last_time_review'\n",
    "# время после последнего отзыва\n",
    "print(df['last_time_review'].describe())\n",
    "df['last_time_review'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:50.497854Z",
     "iopub.status.busy": "2022-08-01T10:21:50.497126Z",
     "iopub.status.idle": "2022-08-01T10:21:50.911139Z",
     "shell.execute_reply": "2022-08-01T10:21:50.909944Z",
     "shell.execute_reply.started": "2022-08-01T10:21:50.497780Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea8d66710>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "perc25 = df['last_time_review'].quantile(0.25)\n",
    "perc75 = df['last_time_review'].quantile(0.75)\n",
    "IQR = perc75 - perc25\n",
    "\n",
    "df['last_time_review'] = df['last_time_review'].apply(lambda x : x if x < perc75 + 1.5*IQR else perc75 + 1.5*IQR)\n",
    "df['last_time_review'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:50.913773Z",
     "iopub.status.busy": "2022-08-01T10:21:50.913097Z",
     "iopub.status.idle": "2022-08-01T10:21:51.384055Z",
     "shell.execute_reply": "2022-08-01T10:21:51.382849Z",
     "shell.execute_reply.started": "2022-08-01T10:21:50.913701Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea8c4a898>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# сгладим этот некрасивый график логарифмом\n",
    "df['last_time_review'] = df['last_time_review'].apply(lambda x: np.log(x) if x != 0 else x)\n",
    "df['last_time_review'].hist(bins=20)\n",
    "# распределение далеко от нормального. Посмотрим, возможно, в дальнейшем избавимся от данного столбца."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:51.386750Z",
     "iopub.status.busy": "2022-08-01T10:21:51.386059Z",
     "iopub.status.idle": "2022-08-01T10:21:51.758051Z",
     "shell.execute_reply": "2022-08-01T10:21:51.756829Z",
     "shell.execute_reply.started": "2022-08-01T10:21:51.386677Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    50000.000000\n",
      "mean         2.709720\n",
      "std          1.785744\n",
      "min          1.000000\n",
      "25%          1.000000\n",
      "50%          2.000000\n",
      "75%          4.000000\n",
      "max         21.000000\n",
      "Name: cuisines_number, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea8c64128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'cuisines_number'\n",
    "# количество кухонь в ресторане\n",
    "print(df['cuisines_number'].describe())\n",
    "df['cuisines_number'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:51.763468Z",
     "iopub.status.busy": "2022-08-01T10:21:51.762745Z",
     "iopub.status.idle": "2022-08-01T10:21:52.273677Z",
     "shell.execute_reply": "2022-08-01T10:21:52.272131Z",
     "shell.execute_reply.started": "2022-08-01T10:21:51.763388Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea8be29b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# сгладим этот некрасивый график логарифмом\n",
    "df['cuisines_number'] = df['cuisines_number'].apply(lambda x: np.log(x) if x != 0 else x)\n",
    "df['cuisines_number'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:52.277164Z",
     "iopub.status.busy": "2022-08-01T10:21:52.276452Z",
     "iopub.status.idle": "2022-08-01T10:21:52.623656Z",
     "shell.execute_reply": "2022-08-01T10:21:52.622480Z",
     "shell.execute_reply.started": "2022-08-01T10:21:52.276904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdec1382630>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# добавим несколько признаков\n",
    "# место ресторана в общем кол-ве ресторанов города\n",
    "df['rest_in_rests'] = df['Ranking']/df['rest_number']\n",
    "df['rest_in_rests'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:52.626743Z",
     "iopub.status.busy": "2022-08-01T10:21:52.625888Z",
     "iopub.status.idle": "2022-08-01T10:21:52.972779Z",
     "shell.execute_reply": "2022-08-01T10:21:52.971572Z",
     "shell.execute_reply.started": "2022-08-01T10:21:52.626663Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdec1286940>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# место ресторана в населении города\n",
    "df['rest_in_pop'] = df['Ranking']/df['population']\n",
    "df['rest_in_pop'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:52.975933Z",
     "iopub.status.busy": "2022-08-01T10:21:52.975057Z",
     "iopub.status.idle": "2022-08-01T10:21:53.307219Z",
     "shell.execute_reply": "2022-08-01T10:21:53.305846Z",
     "shell.execute_reply.started": "2022-08-01T10:21:52.975851Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea0568e48>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# место ресторана в площади города\n",
    "df['rest_in_sq'] = df['Ranking']/df['city_square']\n",
    "df['rest_in_sq'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:53.310225Z",
     "iopub.status.busy": "2022-08-01T10:21:53.309405Z",
     "iopub.status.idle": "2022-08-01T10:21:53.660241Z",
     "shell.execute_reply": "2022-08-01T10:21:53.659127Z",
     "shell.execute_reply.started": "2022-08-01T10:21:53.310139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    50000.000000\n",
      "mean       556.243029\n",
      "std        546.678679\n",
      "min         15.930000\n",
      "25%        105.400000\n",
      "50%        368.000000\n",
      "75%        891.680000\n",
      "max       2586.400000\n",
      "Name: city_square, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea04efeb8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'city_square'\n",
    "# площадь города\n",
    "print(df['city_square'].describe())\n",
    "df['city_square'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:53.662848Z",
     "iopub.status.busy": "2022-08-01T10:21:53.662160Z",
     "iopub.status.idle": "2022-08-01T10:21:54.013706Z",
     "shell.execute_reply": "2022-08-01T10:21:54.012568Z",
     "shell.execute_reply.started": "2022-08-01T10:21:53.662778Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    50000.00000\n",
      "mean         3.96267\n",
      "std          0.54709\n",
      "min          1.00000\n",
      "25%          4.00000\n",
      "50%          4.00000\n",
      "75%          4.00000\n",
      "max          5.00000\n",
      "Name: rating, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea0410d68>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rating\n",
    "# rating - спарсенный рейтинг ресторанов (Nan были заменены медианой)\n",
    "print(df['rating'].describe())\n",
    "df['rating'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:54.016258Z",
     "iopub.status.busy": "2022-08-01T10:21:54.015622Z",
     "iopub.status.idle": "2022-08-01T10:21:55.947421Z",
     "shell.execute_reply": "2022-08-01T10:21:55.946333Z",
     "shell.execute_reply.started": "2022-08-01T10:21:54.016192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdea03977b8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# итак числовые признаки:\n",
    "num_features = ['Number of Reviews', 'time_between', 'last_time_review', \\\n",
    "                'cuisines_number', 'population', 'city_square', 'Ranking', 'rating', 'rest_number', \\\n",
    "                'rest_in_rests', 'rest_in_pop', 'rest_in_sq', 'excelent', 'very_good', 'average', 'poor', 'terrible']\n",
    "\n",
    "# Посмотрим матрицу корреляций\n",
    "correlation_matrix = df[num_features].corr()\n",
    "\n",
    "# выведем на тепловую карту\n",
    "plt.figure(figsize=(18,12))\n",
    "sns.heatmap(correlation_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:55.949308Z",
     "iopub.status.busy": "2022-08-01T10:21:55.949014Z",
     "iopub.status.idle": "2022-08-01T10:21:57.009155Z",
     "shell.execute_reply": "2022-08-01T10:21:57.007988Z",
     "shell.execute_reply.started": "2022-08-01T10:21:55.949260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_features = ['City', 'Cuisine Style', 'Price Range']\n",
    "bin_features = ['alive', 'is_capital', 'net_rest']\n",
    "#Посмотрим боксплоты у бинарных признаков\n",
    "def get_boxplot(column):\n",
    "    fig, ax = plt.subplots(figsize = (14, 4))\n",
    "    sns.boxplot(x=column, y='rating', \n",
    "                data=df[df[column].isin(df[column].value_counts().index[:])],\n",
    "               ax=ax)\n",
    "    plt.xticks(rotation=45)\n",
    "    ax.set_title('Boxplot for ' + column)\n",
    "    plt.show()\n",
    "\n",
    "for col in bin_features:\n",
    "    get_boxplot(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:57.012484Z",
     "iopub.status.busy": "2022-08-01T10:21:57.011688Z",
     "iopub.status.idle": "2022-08-01T10:21:57.065153Z",
     "shell.execute_reply": "2022-08-01T10:21:57.064037Z",
     "shell.execute_reply.started": "2022-08-01T10:21:57.012410Z"
    }
   },
   "outputs": [],
   "source": [
    "# Сделаем столбцы из категориальных признаков\n",
    "df = pd.get_dummies(df, columns=['City', 'Price Range'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:21:57.068987Z",
     "iopub.status.busy": "2022-08-01T10:21:57.068661Z",
     "iopub.status.idle": "2022-08-01T10:22:11.082230Z",
     "shell.execute_reply": "2022-08-01T10:22:11.081092Z",
     "shell.execute_reply.started": "2022-08-01T10:21:57.068928Z"
    }
   },
   "outputs": [],
   "source": [
    "# С Cuisine Style чуть сложнее. Переведем в списки, добавим столбцы, заполним по наличию кухни в списке\n",
    "df['Cuisine Style'] = df['Cuisine Style'].apply(lambda x: x[2:-2].split(\"', '\"))\n",
    "cuisines_list = []\n",
    "for cuisines in df['Cuisine Style']:\n",
    "    for cuisine in cuisines: \n",
    "        if cuisine not in cuisines_list:\n",
    "            cuisines_list.append(cuisine)\n",
    "\n",
    "for cuisine in cuisines_list:\n",
    "    df = df.reindex(columns = np.append(df.columns.values, [cuisine]))\n",
    "\n",
    "\n",
    "for cuisine in cuisines_list:    \n",
    "    df[cuisine] = df['Cuisine Style'].apply(lambda x: 1 if cuisine in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.084288Z",
     "iopub.status.busy": "2022-08-01T10:22:11.083872Z",
     "iopub.status.idle": "2022-08-01T10:22:11.260934Z",
     "shell.execute_reply": "2022-08-01T10:22:11.259703Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.084201Z"
    }
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Restaurant_id</th>\n",
       "      <th>Cuisine Style</th>\n",
       "      <th>Ranking</th>\n",
       "      <th>Rating</th>\n",
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       "      <th>ID_TA</th>\n",
       "      <th>Main</th>\n",
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       "      <th>very_good</th>\n",
       "      <th>average</th>\n",
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       "      <th>cuisines_number</th>\n",
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       "      <th>city_square</th>\n",
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       "      <th>compound</th>\n",
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       "      <th>City_Athens</th>\n",
       "      <th>City_Barcelona</th>\n",
       "      <th>City_Berlin</th>\n",
       "      <th>City_Bratislava</th>\n",
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       "      <th>City_Budapest</th>\n",
       "      <th>City_Copenhagen</th>\n",
       "      <th>City_Dublin</th>\n",
       "      <th>City_Edinburgh</th>\n",
       "      <th>City_Geneva</th>\n",
       "      <th>City_Hamburg</th>\n",
       "      <th>City_Helsinki</th>\n",
       "      <th>City_Krakow</th>\n",
       "      <th>City_Lisbon</th>\n",
       "      <th>City_Ljubljana</th>\n",
       "      <th>City_London</th>\n",
       "      <th>City_Luxembourg</th>\n",
       "      <th>City_Lyon</th>\n",
       "      <th>City_Madrid</th>\n",
       "      <th>City_Milan</th>\n",
       "      <th>City_Munich</th>\n",
       "      <th>City_Oporto</th>\n",
       "      <th>City_Oslo</th>\n",
       "      <th>City_Paris</th>\n",
       "      <th>City_Prague</th>\n",
       "      <th>City_Rome</th>\n",
       "      <th>City_Stockholm</th>\n",
       "      <th>City_Vienna</th>\n",
       "      <th>City_Warsaw</th>\n",
       "      <th>City_Zurich</th>\n",
       "      <th>Price Range_$</th>\n",
       "      <th>Price Range_$$ - $$$</th>\n",
       "      <th>Price Range_$$$$</th>\n",
       "      <th>Price Range_$*</th>\n",
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       "      <th>French</th>\n",
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       "      <th>Central European</th>\n",
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       "      <th>Portuguese</th>\n",
       "      <th>Wine Bar</th>\n",
       "      <th>Italian</th>\n",
       "      <th>Pizza</th>\n",
       "      <th>Fast Food</th>\n",
       "      <th>Mediterranean</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Healthy</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Thai</th>\n",
       "      <th>Vietnamese</th>\n",
       "      <th>Pub</th>\n",
       "      <th>Chinese</th>\n",
       "      <th>British</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Fusion</th>\n",
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       "      <th>Dutch</th>\n",
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       "      <th>Venezuelan</th>\n",
       "      <th>South American</th>\n",
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       "      <th>Steakhouse</th>\n",
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       "      <th>Barbecue</th>\n",
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       "      <th>Irish</th>\n",
       "      <th>Seafood</th>\n",
       "      <th>Hungarian</th>\n",
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       "      <th>Greek</th>\n",
       "      <th>Central American</th>\n",
       "      <th>Indian</th>\n",
       "      <th>Middle Eastern</th>\n",
       "      <th>Turkish</th>\n",
       "      <th>Eastern European</th>\n",
       "      <th>Pakistani</th>\n",
       "      <th>Peruvian</th>\n",
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       "      <th>Tibetan</th>\n",
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       "      <th>Czech</th>\n",
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       "      <th>Canadian</th>\n",
       "      <th>Romana</th>\n",
       "      <th>Beer restaurants</th>\n",
       "      <th>Salvadoran</th>\n",
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       "      <th>Dining bars</th>\n",
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       "      <th>23460</th>\n",
       "      <td>id_1509</td>\n",
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       "      <td>1511.0</td>\n",
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       "      <th>24078</th>\n",
       "      <td>id_354</td>\n",
       "      <td>[European, Vegetarian Friendly, Vegan Options]</td>\n",
       "      <td>355.0</td>\n",
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       "      <td>2.484907</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Restaurant_id                                    Cuisine Style  Ranking  \\\n",
       "23460       id_1509  [Steakhouse, Pizza, Cafe, Street Food, Italian]   1511.0   \n",
       "24078        id_354   [European, Vegetarian Friendly, Vegan Options]    355.0   \n",
       "\n",
       "       Rating  Number of Reviews      ID_TA  Main  alive  rating  excelent  \\\n",
       "23460     3.5           3.637586   d1021090  True      1     4.0       1.0   \n",
       "24078     4.0           2.484907  d13170250  True      1     4.0      49.0   \n",
       "\n",
       "       very_good  average  poor  terrible  time_between  last_time_review  \\\n",
       "23460        4.0      1.0   2.0       0.0      4.204693          7.658228   \n",
       "24078       31.0     16.0  18.0       8.0      1.386294          7.402452   \n",
       "\n",
       "                                            text_reviews  rest_number  \\\n",
       "23460            'Very pricey', 'Far below expectations'       2460.0   \n",
       "24078  'Definitely Yes!', 'Nice veggie food, ok atmos...       1399.0   \n",
       "\n",
       "       is_capital  cuisines_number  population  city_square  net_rest  \\\n",
       "23460           1         1.609438     3168846       412.00         1   \n",
       "24078           1         1.098612      574579       715.48         1   \n",
       "\n",
       "       negative  neutra  positive  compound  rest_in_rests  rest_in_pop  \\\n",
       "23460       0.0   1.000     0.000    0.0000       0.614228     0.000477   \n",
       "24078       0.0   0.334     0.666    0.7901       0.253753     0.000618   \n",
       "\n",
       "       rest_in_sq  City_Amsterdam  City_Athens  City_Barcelona  City_Berlin  \\\n",
       "23460    3.667476               0            1               0            0   \n",
       "24078    0.496170               0            0               0            0   \n",
       "\n",
       "       City_Bratislava  City_Brussels  City_Budapest  City_Copenhagen  \\\n",
       "23460                0              0              0                0   \n",
       "24078                0              0              0                0   \n",
       "\n",
       "       City_Dublin  City_Edinburgh  City_Geneva  City_Hamburg  City_Helsinki  \\\n",
       "23460            0               0            0             0              0   \n",
       "24078            0               0            0             0              1   \n",
       "\n",
       "       City_Krakow  City_Lisbon  City_Ljubljana  City_London  City_Luxembourg  \\\n",
       "23460            0            0               0            0                0   \n",
       "24078            0            0               0            0                0   \n",
       "\n",
       "       City_Lyon  City_Madrid  City_Milan  City_Munich  City_Oporto  \\\n",
       "23460          0            0           0            0            0   \n",
       "24078          0            0           0            0            0   \n",
       "\n",
       "       City_Oslo  City_Paris  City_Prague  City_Rome  City_Stockholm  \\\n",
       "23460          0           0            0          0               0   \n",
       "24078          0           0            0          0               0   \n",
       "\n",
       "       City_Vienna  City_Warsaw  City_Zurich  Price Range_$  \\\n",
       "23460            0            0            0              0   \n",
       "24078            0            0            0              0   \n",
       "\n",
       "       Price Range_$$ - $$$  Price Range_$$$$  Price Range_$*  European  \\\n",
       "23460                     1                 0               0         0   \n",
       "24078                     1                 0               0         1   \n",
       "\n",
       "       French  International  Asian  Nepali  Japanese  Sushi  Grill  \\\n",
       "23460       0              0      0       0         0      0      0   \n",
       "24078       0              0      0       0         0      0      0   \n",
       "\n",
       "       Vegetarian Friendly  Vegan Options  Gluten Free Options  usual  German  \\\n",
       "23460                    0              0                    0      0       0   \n",
       "24078                    1              1                    0      0       0   \n",
       "\n",
       "       Central European  Bar  Portuguese  Wine Bar  Italian  Pizza  Fast Food  \\\n",
       "23460                 0    0           0         0        1      1          0   \n",
       "24078                 0    0           0         0        0      0          0   \n",
       "\n",
       "       Mediterranean  Spanish  Healthy  Cafe  Thai  Vietnamese  Pub  Chinese  \\\n",
       "23460              0        0        0     1     0           0    0        0   \n",
       "24078              0        0        0     0     0           0    0        0   \n",
       "\n",
       "       British  Polish  Fusion  American  Diner  Dutch  Mexican  Venezuelan  \\\n",
       "23460        0       0       0         0      0      0        0           0   \n",
       "24078        0       0       0         0      0      0        0           0   \n",
       "\n",
       "       South American  Soups  Belgian  Steakhouse  Latin  Barbecue  \\\n",
       "23460               0      0        0           1      0         0   \n",
       "24078               0      0        0           0      0         0   \n",
       "\n",
       "       Argentinean  Irish  Seafood  Hungarian  Swiss  Contemporary  Greek  \\\n",
       "23460            0      0        0          0      0             0      0   \n",
       "24078            0      0        0          0      0             0      0   \n",
       "\n",
       "       Central American  Indian  Middle Eastern  Turkish  Eastern European  \\\n",
       "23460                 0       0               0        0                 0   \n",
       "24078                 0       0               0        0                 0   \n",
       "\n",
       "       Pakistani  Peruvian  Delicatessen  Swedish  Scandinavian  Tibetan  \\\n",
       "23460          0         0             0        0             0        0   \n",
       "24078          0         0             0        0             0        0   \n",
       "\n",
       "       Korean  Southwestern  Czech  Slovenian  Balti  Street Food  \\\n",
       "23460       0             0      0          0      0            1   \n",
       "24078       0             0      0          0      0            0   \n",
       "\n",
       "       Quick Bites  Basque  Brew Pub  Caribbean  Austrian  Moroccan  Halal  \\\n",
       "23460            0       0         0          0         0         0      0   \n",
       "24078            0       0         0          0         0         0      0   \n",
       "\n",
       "       Lebanese  Russian  African  Ethiopian  Egyptian  Danish  Brazilian  \\\n",
       "23460         0        0        0          0         0       0          0   \n",
       "24078         0        0        0          0         0       0          0   \n",
       "\n",
       "       Ecuadorean  Israeli  Kosher  Gastropub  Australian  Singaporean  \\\n",
       "23460           0        0       0          0           0            0   \n",
       "24078           0        0       0          0           0            0   \n",
       "\n",
       "       Malaysian  Minority Chinese  Scottish  Arabic  Ukrainian  Chilean  \\\n",
       "23460          0                 0         0       0          0        0   \n",
       "24078          0                 0         0       0          0        0   \n",
       "\n",
       "       Mongolian  Emilian  Cuban  Dessert  Persian  Croatian  Filipino  \\\n",
       "23460          0        0      0        0        0         0         0   \n",
       "24078          0        0      0        0        0         0         0   \n",
       "\n",
       "       Indonesian  Colombian  Jamaican  Norwegian  Hawaiian  Bakeries  Deli  \\\n",
       "23460           0          0         0          0         0         0     0   \n",
       "24078           0          0         0          0         0         0     0   \n",
       "\n",
       "       Armenian  Taiwanese  Bangladeshi  Cajun & Creole  Sri Lankan  \\\n",
       "23460         0          0            0               0           0   \n",
       "24078         0          0            0               0           0   \n",
       "\n",
       "       Cambodian  Albanian  New Zealand  Central Asian  Specialty Food Market  \\\n",
       "23460          0         0            0              0                      0   \n",
       "24078          0         0            0              0                      0   \n",
       "\n",
       "       Tunisian  Romanian  Georgian  Romagna  Polynesian  Catalan  \\\n",
       "23460         0         0         0        0           0        0   \n",
       "24078         0         0         0        0           0        0   \n",
       "\n",
       "       Azerbaijani  Caucasian  Afghani  Uzbek  Canadian  Romana  \\\n",
       "23460            0          0        0      0         0       0   \n",
       "24078            0          0        0      0         0       0   \n",
       "\n",
       "       Beer restaurants  Salvadoran  Yunnan  Native American  Dining bars  \\\n",
       "23460                 0           0       0                0            0   \n",
       "24078                 0           0       0                0            0   \n",
       "\n",
       "       Xinjiang  Burmese  Lombard  Northern-Italian  Fujian  Neapolitan  \\\n",
       "23460         0        0        0                 0       0           0   \n",
       "24078         0        0        0                 0       0           0   \n",
       "\n",
       "       Campania  Welsh  Lazio  Cantonese  Beijing Specialties  Latvian  \\\n",
       "23460         0      0      0          0                    0        0   \n",
       "24078         0      0      0          0                    0        0   \n",
       "\n",
       "       Japanese Fusion  Sardinian  Southern-Italian  \n",
       "23460                0          0                 0  \n",
       "24078                0          0                 0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with pd.option_context('display.max_columns', None):\n",
    "    display(df.sample(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.267000Z",
     "iopub.status.busy": "2022-08-01T10:22:11.266653Z",
     "iopub.status.idle": "2022-08-01T10:22:11.276637Z",
     "shell.execute_reply": "2022-08-01T10:22:11.274919Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.266946Z"
    }
   },
   "outputs": [],
   "source": [
    "df.last_time_review = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.281935Z",
     "iopub.status.busy": "2022-08-01T10:22:11.281475Z",
     "iopub.status.idle": "2022-08-01T10:22:11.703382Z",
     "shell.execute_reply": "2022-08-01T10:22:11.701971Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.281855Z"
    }
   },
   "outputs": [],
   "source": [
    "# оставляем в табличке для теста только рабочие столбцы \n",
    "df_for_test = df.drop(['Restaurant_id', 'Cuisine Style', 'ID_TA', 'text_reviews', 'Rating'], axis = 1)\n",
    "# df_for_test = df.drop(['Restaurant_id', 'Cuisine Style', 'ID_TA', 'text_reviews', 'Rating', 'last_time_review'], axis = 1)\n",
    "scaler = MinMaxScaler()\n",
    "df_for_test[num_features] = scaler.fit_transform(df_for_test[num_features])\n",
    "\n",
    "\n",
    "# Готовим данные\n",
    "X = df_for_test[df_for_test['Main']]\n",
    "y = df[df['Main']]['Rating']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- # DATA -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.705847Z",
     "iopub.status.busy": "2022-08-01T10:22:11.705394Z",
     "iopub.status.idle": "2022-08-01T10:22:11.711028Z",
     "shell.execute_reply": "2022-08-01T10:22:11.709735Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.705751Z"
    }
   },
   "outputs": [],
   "source": [
    "# DATA_DIR = '/kaggle/input/sf-dst-restaurant-rating/'\n",
    "# df_train = pd.read_csv(DATA_DIR+'/main_task.csv')\n",
    "# df_test = pd.read_csv(DATA_DIR+'kaggle_task.csv')\n",
    "# sample_submission = pd.read_csv(DATA_DIR+'/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.713421Z",
     "iopub.status.busy": "2022-08-01T10:22:11.713001Z",
     "iopub.status.idle": "2022-08-01T10:22:11.725536Z",
     "shell.execute_reply": "2022-08-01T10:22:11.724285Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.713347Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.728003Z",
     "iopub.status.busy": "2022-08-01T10:22:11.727324Z",
     "iopub.status.idle": "2022-08-01T10:22:11.738658Z",
     "shell.execute_reply": "2022-08-01T10:22:11.737514Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.727944Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.740956Z",
     "iopub.status.busy": "2022-08-01T10:22:11.740423Z",
     "iopub.status.idle": "2022-08-01T10:22:11.752089Z",
     "shell.execute_reply": "2022-08-01T10:22:11.750777Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.740857Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.754684Z",
     "iopub.status.busy": "2022-08-01T10:22:11.754021Z",
     "iopub.status.idle": "2022-08-01T10:22:11.764900Z",
     "shell.execute_reply": "2022-08-01T10:22:11.763909Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.754380Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_test.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.767105Z",
     "iopub.status.busy": "2022-08-01T10:22:11.766739Z",
     "iopub.status.idle": "2022-08-01T10:22:11.778245Z",
     "shell.execute_reply": "2022-08-01T10:22:11.777206Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.767046Z"
    }
   },
   "outputs": [],
   "source": [
    "# sample_submission.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.782322Z",
     "iopub.status.busy": "2022-08-01T10:22:11.781929Z",
     "iopub.status.idle": "2022-08-01T10:22:11.791953Z",
     "shell.execute_reply": "2022-08-01T10:22:11.790829Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.782270Z"
    }
   },
   "outputs": [],
   "source": [
    "# sample_submission.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.794246Z",
     "iopub.status.busy": "2022-08-01T10:22:11.793841Z",
     "iopub.status.idle": "2022-08-01T10:22:11.805165Z",
     "shell.execute_reply": "2022-08-01T10:22:11.804313Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.794171Z"
    }
   },
   "outputs": [],
   "source": [
    "# # ВАЖНО! дря корректной обработки признаков объединяем трейн и тест в один датасет\n",
    "# df_train['sample'] = 1 # помечаем где у нас трейн\n",
    "# df_test['sample'] = 0 # помечаем где у нас тест\n",
    "# df_test['Rating'] = 0 # в тесте у нас нет значения Rating, мы его должны предсказать, по этому пока просто заполняем нулями\n",
    "\n",
    "# data = df_test.append(df_train, sort=False).reset_index(drop=True) # объединяем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.806537Z",
     "iopub.status.busy": "2022-08-01T10:22:11.806282Z",
     "iopub.status.idle": "2022-08-01T10:22:11.824469Z",
     "shell.execute_reply": "2022-08-01T10:22:11.823617Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.806497Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Подробнее по признакам:\n",
    "* City: Город \n",
    "* Cuisine Style: Кухня\n",
    "* Ranking: Ранг ресторана относительно других ресторанов в этом городе\n",
    "* Price Range: Цены в ресторане в 3 категориях\n",
    "* Number of Reviews: Количество отзывов\n",
    "* Reviews: 2 последних отзыва и даты этих отзывов\n",
    "* URL_TA: страница ресторана на 'www.tripadvisor.com' \n",
    "* ID_TA: ID ресторана в TripAdvisor\n",
    "* Rating: Рейтинг ресторана"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.826387Z",
     "iopub.status.busy": "2022-08-01T10:22:11.826057Z",
     "iopub.status.idle": "2022-08-01T10:22:11.837682Z",
     "shell.execute_reply": "2022-08-01T10:22:11.836560Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.826324Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.839403Z",
     "iopub.status.busy": "2022-08-01T10:22:11.839122Z",
     "iopub.status.idle": "2022-08-01T10:22:11.851129Z",
     "shell.execute_reply": "2022-08-01T10:22:11.850362Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.839361Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.Reviews[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим, большинство признаков у нас требует очистки и предварительной обработки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning and Prepping Data\n",
    "Обычно данные содержат в себе кучу мусора, который необходимо почистить, для того чтобы привести их в приемлемый формат. Чистка данных — это необходимый этап решения почти любой реальной задачи.   \n",
    "![](https://analyticsindiamag.com/wp-content/uploads/2018/01/data-cleaning.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Обработка NAN \n",
    "У наличия пропусков могут быть разные причины, но пропуски нужно либо заполнить, либо исключить из набора полностью. Но с пропусками нужно быть внимательным, **даже отсутствие информации может быть важным признаком!**   \n",
    "По этому перед обработкой NAN лучше вынести информацию о наличии пропуска как отдельный признак "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.853208Z",
     "iopub.status.busy": "2022-08-01T10:22:11.852691Z",
     "iopub.status.idle": "2022-08-01T10:22:11.862712Z",
     "shell.execute_reply": "2022-08-01T10:22:11.861592Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.853141Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Для примера я возьму столбец Number of Reviews\n",
    "# data['Number_of_Reviews_isNAN'] = pd.isna(data['Number of Reviews']).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.864727Z",
     "iopub.status.busy": "2022-08-01T10:22:11.864138Z",
     "iopub.status.idle": "2022-08-01T10:22:11.876441Z",
     "shell.execute_reply": "2022-08-01T10:22:11.875282Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.864443Z"
    }
   },
   "outputs": [],
   "source": [
    "# data['Number_of_Reviews_isNAN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.878790Z",
     "iopub.status.busy": "2022-08-01T10:22:11.878272Z",
     "iopub.status.idle": "2022-08-01T10:22:11.891239Z",
     "shell.execute_reply": "2022-08-01T10:22:11.890068Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.878724Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Далее заполняем пропуски 0, вы можете попробовать заполнением средним или средним по городу и тд...\n",
    "# data['Number of Reviews'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Обработка признаков\n",
    "Для начала посмотрим какие признаки у нас могут быть категориальными."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.893275Z",
     "iopub.status.busy": "2022-08-01T10:22:11.892957Z",
     "iopub.status.idle": "2022-08-01T10:22:11.902966Z",
     "shell.execute_reply": "2022-08-01T10:22:11.901873Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.893225Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.nunique(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какие признаки можно считать категориальными?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для кодирования категориальных признаков есть множество подходов:\n",
    "* Label Encoding\n",
    "* One-Hot Encoding\n",
    "* Target Encoding\n",
    "* Hashing\n",
    "\n",
    "Выбор кодирования зависит от признака и выбраной модели.\n",
    "Не будем сейчас сильно погружаться в эту тематику, давайте посмотрим лучше пример с One-Hot Encoding:\n",
    "![](https://i.imgur.com/mtimFxh.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.905109Z",
     "iopub.status.busy": "2022-08-01T10:22:11.904800Z",
     "iopub.status.idle": "2022-08-01T10:22:11.914818Z",
     "shell.execute_reply": "2022-08-01T10:22:11.913936Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.905056Z"
    }
   },
   "outputs": [],
   "source": [
    "# # для One-Hot Encoding в pandas есть готовая функция - get_dummies. Особенно радует параметр dummy_na\n",
    "# data = pd.get_dummies(data, columns=[ 'City',], dummy_na=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.916728Z",
     "iopub.status.busy": "2022-08-01T10:22:11.916366Z",
     "iopub.status.idle": "2022-08-01T10:22:11.929229Z",
     "shell.execute_reply": "2022-08-01T10:22:11.928331Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.916665Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.930638Z",
     "iopub.status.busy": "2022-08-01T10:22:11.930357Z",
     "iopub.status.idle": "2022-08-01T10:22:11.941116Z",
     "shell.execute_reply": "2022-08-01T10:22:11.940127Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.930574Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Возьмем следующий признак \"Price Range\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.942735Z",
     "iopub.status.busy": "2022-08-01T10:22:11.942434Z",
     "iopub.status.idle": "2022-08-01T10:22:11.953486Z",
     "shell.execute_reply": "2022-08-01T10:22:11.952361Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.942692Z"
    }
   },
   "outputs": [],
   "source": [
    "# data['Price Range'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По описанию 'Price Range' это - Цены в ресторане.  \n",
    "Их можно поставить по возрастанию (значит это не категориальный признак). А это значит, что их можно заменить последовательными числами, например 1,2,3  \n",
    "*Попробуйте сделать обработку этого признака уже самостоятельно!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.955366Z",
     "iopub.status.busy": "2022-08-01T10:22:11.954916Z",
     "iopub.status.idle": "2022-08-01T10:22:11.967912Z",
     "shell.execute_reply": "2022-08-01T10:22:11.966510Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.955318Z"
    }
   },
   "outputs": [],
   "source": [
    "# Ваша обработка 'Price Range'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Для некоторых алгоритмов МЛ даже для не категориальных признаков можно применить One-Hot Encoding, и это может улучшить качество модели. Пробуйте разные подходы к кодированию признака - никто не знает заранее, что может взлететь."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обработать другие признаки вы должны самостоятельно!\n",
    "Для обработки других признаков вам возможно придется даже написать свою функцию, а может даже и не одну, но в этом и есть ваша практика в этом модуле!     \n",
    "Следуя подсказкам в модуле вы сможете более подробно узнать, как сделать эти приобразования."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.969944Z",
     "iopub.status.busy": "2022-08-01T10:22:11.969403Z",
     "iopub.status.idle": "2022-08-01T10:22:11.981680Z",
     "shell.execute_reply": "2022-08-01T10:22:11.980512Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.969870Z"
    }
   },
   "outputs": [],
   "source": [
    "# тут ваш код на обработку других признаков\n",
    "# ....."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cs10.pikabu.ru/post_img/2018/09/06/11/1536261023140110012.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA \n",
    "[Exploratory Data Analysis](https://ru.wikipedia.org/wiki/Разведочный_анализ_данных) - Анализ данных\n",
    "На этом этапе мы строим графики, ищем закономерности, аномалии, выбросы или связи между признаками.\n",
    "В общем цель этого этапа понять, что эти данные могут нам дать и как признаки могут быть взаимосвязаны между собой.\n",
    "Понимание изначальных признаков позволит сгенерировать новые, более сильные и, тем самым, сделать нашу модель лучше.\n",
    "![](https://miro.medium.com/max/2598/1*RXdMb7Uk6mGqWqPguHULaQ.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим распределение признака"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.984855Z",
     "iopub.status.busy": "2022-08-01T10:22:11.983437Z",
     "iopub.status.idle": "2022-08-01T10:22:11.996364Z",
     "shell.execute_reply": "2022-08-01T10:22:11.995048Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.984791Z"
    }
   },
   "outputs": [],
   "source": [
    "# plt.rcParams['figure.figsize'] = (10,7)\n",
    "# df_train['Ranking'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У нас много ресторанов, которые не дотягивают и до 2500 места в своем городе, а что там по городам?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:11.998234Z",
     "iopub.status.busy": "2022-08-01T10:22:11.997804Z",
     "iopub.status.idle": "2022-08-01T10:22:12.009447Z",
     "shell.execute_reply": "2022-08-01T10:22:12.008442Z",
     "shell.execute_reply.started": "2022-08-01T10:22:11.998190Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_train['City'].value_counts(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А кто-то говорил, что французы любят поесть=) Посмотрим, как изменится распределение в большом городе:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.011550Z",
     "iopub.status.busy": "2022-08-01T10:22:12.011088Z",
     "iopub.status.idle": "2022-08-01T10:22:12.023186Z",
     "shell.execute_reply": "2022-08-01T10:22:12.022298Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.011352Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_train['Ranking'][df_train['City'] =='London'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.025052Z",
     "iopub.status.busy": "2022-08-01T10:22:12.024547Z",
     "iopub.status.idle": "2022-08-01T10:22:12.036626Z",
     "shell.execute_reply": "2022-08-01T10:22:12.035813Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.024993Z"
    }
   },
   "outputs": [],
   "source": [
    "# # посмотрим на топ 10 городов\n",
    "# for x in (df_train['City'].value_counts())[0:10].index:\n",
    "#     df_train['Ranking'][df_train['City'] == x].hist(bins=100)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получается, что Ranking имеет нормальное распределение, просто в больших городах больше ресторанов, из-за мы этого имеем смещение.\n",
    "\n",
    ">Подумайте как из этого можно сделать признак для вашей модели. Я покажу вам пример, как визуализация помогает находить взаимосвязи. А далее действуйте без подсказок =) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим распределение целевой переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.038636Z",
     "iopub.status.busy": "2022-08-01T10:22:12.038113Z",
     "iopub.status.idle": "2022-08-01T10:22:12.049722Z",
     "shell.execute_reply": "2022-08-01T10:22:12.048814Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.038544Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_train['Rating'].value_counts(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим распределение целевой переменной относительно признака"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.051753Z",
     "iopub.status.busy": "2022-08-01T10:22:12.051185Z",
     "iopub.status.idle": "2022-08-01T10:22:12.065004Z",
     "shell.execute_reply": "2022-08-01T10:22:12.064139Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.051700Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_train['Ranking'][df_train['Rating'] == 5].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.066842Z",
     "iopub.status.busy": "2022-08-01T10:22:12.066349Z",
     "iopub.status.idle": "2022-08-01T10:22:12.078418Z",
     "shell.execute_reply": "2022-08-01T10:22:12.077487Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.066792Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_train['Ranking'][df_train['Rating'] < 4].hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### И один из моих любимых - [корреляция признаков](https://ru.wikipedia.org/wiki/Корреляция)\n",
    "На этом графике уже сейчас вы сможете заметить, как признаки связаны между собой и с целевой переменной."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.080215Z",
     "iopub.status.busy": "2022-08-01T10:22:12.079653Z",
     "iopub.status.idle": "2022-08-01T10:22:12.090430Z",
     "shell.execute_reply": "2022-08-01T10:22:12.089466Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.080149Z"
    }
   },
   "outputs": [],
   "source": [
    "# plt.rcParams['figure.figsize'] = (15,10)\n",
    "# sns.heatmap(data.drop(['sample'], axis=1).corr(),)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вообще благодаря визуализации в этом датасете можно узнать много интересных фактов, например:\n",
    "* где больше Пицерий в Мадриде или Лондоне?\n",
    "* в каком городе кухня ресторанов более разнообразна?\n",
    "\n",
    "придумайте свои вопрос и найдите на него ответ в данных)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "Теперь, для удобства и воспроизводимости кода, завернем всю обработку в одну большую функцию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.092851Z",
     "iopub.status.busy": "2022-08-01T10:22:12.092490Z",
     "iopub.status.idle": "2022-08-01T10:22:12.102964Z",
     "shell.execute_reply": "2022-08-01T10:22:12.101941Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.092790Z"
    }
   },
   "outputs": [],
   "source": [
    "# # на всякий случай, заново подгружаем данные\n",
    "# df_train = pd.read_csv(DATA_DIR+'/main_task.csv')\n",
    "# df_test = pd.read_csv(DATA_DIR+'/kaggle_task.csv')\n",
    "# df_train['sample'] = 1 # помечаем где у нас трейн\n",
    "# df_test['sample'] = 0 # помечаем где у нас тест\n",
    "# df_test['Rating'] = 0 # в тесте у нас нет значения Rating, мы его должны предсказать, по этому пока просто заполняем нулями\n",
    "\n",
    "# data = df_test.append(df_train, sort=False).reset_index(drop=True) # объединяем\n",
    "# data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.105479Z",
     "iopub.status.busy": "2022-08-01T10:22:12.105102Z",
     "iopub.status.idle": "2022-08-01T10:22:12.116712Z",
     "shell.execute_reply": "2022-08-01T10:22:12.115727Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.105416Z"
    }
   },
   "outputs": [],
   "source": [
    "def preproc_data(df_input):\n",
    "    '''includes several functions to pre-process the predictor data.'''\n",
    "    \n",
    "    df_output = df_input.copy()\n",
    "    \n",
    "    # ################### 1. Предобработка ############################################################## \n",
    "    # убираем не нужные для модели признаки\n",
    "    df_output.drop(['Restaurant_id','ID_TA',], axis = 1, inplace=True)\n",
    "    \n",
    "    \n",
    "    # ################### 2. NAN ############################################################## \n",
    "    # Далее заполняем пропуски, вы можете попробовать заполнением средним или средним по городу и тд...\n",
    "    df_output['Number of Reviews'].fillna(0, inplace=True)\n",
    "    # тут ваш код по обработке NAN\n",
    "    # ....\n",
    "    \n",
    "    \n",
    "    # ################### 3. Encoding ############################################################## \n",
    "    # для One-Hot Encoding в pandas есть готовая функция - get_dummies. Особенно радует параметр dummy_na\n",
    "    df_output = pd.get_dummies(df_output, columns=[ 'City',], dummy_na=True)\n",
    "    # тут ваш код не Encoding фитчей\n",
    "    # ....\n",
    "    \n",
    "    \n",
    "    # ################### 4. Feature Engineering ####################################################\n",
    "    # тут ваш код не генерацию новых фитчей\n",
    "    # ....\n",
    "    \n",
    "    \n",
    "    # ################### 5. Clean #################################################### \n",
    "    # убираем признаки которые еще не успели обработать, \n",
    "    # модель на признаках с dtypes \"object\" обучаться не будет, просто выберим их и удалим\n",
    "    object_columns = [s for s in df_output.columns if df_output[s].dtypes == 'object']\n",
    "    df_output.drop(object_columns, axis = 1, inplace=True)\n",
    "    \n",
    "    return df_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">По хорошему, можно было бы перевести эту большую функцию в класс и разбить на подфункции (согласно ООП). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Запускаем и проверяем что получилось"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.118646Z",
     "iopub.status.busy": "2022-08-01T10:22:12.118309Z",
     "iopub.status.idle": "2022-08-01T10:22:12.132168Z",
     "shell.execute_reply": "2022-08-01T10:22:12.131186Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.118574Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_preproc = preproc_data(data)\n",
    "# df_preproc.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.134242Z",
     "iopub.status.busy": "2022-08-01T10:22:12.133895Z",
     "iopub.status.idle": "2022-08-01T10:22:12.146911Z",
     "shell.execute_reply": "2022-08-01T10:22:12.145670Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.134183Z"
    }
   },
   "outputs": [],
   "source": [
    "# df_preproc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.148480Z",
     "iopub.status.busy": "2022-08-01T10:22:12.148067Z",
     "iopub.status.idle": "2022-08-01T10:22:12.158760Z",
     "shell.execute_reply": "2022-08-01T10:22:12.157887Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.148419Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Теперь выделим тестовую часть\n",
    "# train_data = df_preproc.query('sample == 1').drop(['sample'], axis=1)\n",
    "# test_data = df_preproc.query('sample == 0').drop(['sample'], axis=1)\n",
    "\n",
    "# y = train_data.Rating.values            # наш таргет\n",
    "# X = train_data.drop(['Rating'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Перед тем как отправлять наши данные на обучение, разделим данные на еще один тест и трейн, для валидации. \n",
    "Это поможет нам проверить, как хорошо наша модель работает, до отправки submissiona на kaggle.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.160729Z",
     "iopub.status.busy": "2022-08-01T10:22:12.160335Z",
     "iopub.status.idle": "2022-08-01T10:22:12.236476Z",
     "shell.execute_reply": "2022-08-01T10:22:12.235542Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.160658Z"
    }
   },
   "outputs": [],
   "source": [
    "# Воспользуемся специальной функцие train_test_split для разбивки тестовых данных\n",
    "# выделим 20% данных на валидацию (параметр test_size)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.238486Z",
     "iopub.status.busy": "2022-08-01T10:22:12.238125Z",
     "iopub.status.idle": "2022-08-01T10:22:12.242802Z",
     "shell.execute_reply": "2022-08-01T10:22:12.241701Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.238428Z"
    }
   },
   "outputs": [],
   "source": [
    "# проверяем\n",
    "# test_data.shape, train_data.shape, X.shape, X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model \n",
    "Сам ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.244165Z",
     "iopub.status.busy": "2022-08-01T10:22:12.243890Z",
     "iopub.status.idle": "2022-08-01T10:22:12.255650Z",
     "shell.execute_reply": "2022-08-01T10:22:12.254665Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.244114Z"
    }
   },
   "outputs": [],
   "source": [
    "# Импортируем необходимые библиотеки:\n",
    "# from sklearn.ensemble import RandomForestRegressor # инструмент для создания и обучения модели\n",
    "# from sklearn import metrics # инструменты для оценки точности модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.257593Z",
     "iopub.status.busy": "2022-08-01T10:22:12.257286Z",
     "iopub.status.idle": "2022-08-01T10:22:12.268453Z",
     "shell.execute_reply": "2022-08-01T10:22:12.267472Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.257545Z"
    }
   },
   "outputs": [],
   "source": [
    "# Создаём модель (НАСТРОЙКИ НЕ ТРОГАЕМ)\n",
    "model = RandomForestRegressor(n_estimators=100, verbose=1, n_jobs=-1, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:12.270432Z",
     "iopub.status.busy": "2022-08-01T10:22:12.269958Z",
     "iopub.status.idle": "2022-08-01T10:22:44.527111Z",
     "shell.execute_reply": "2022-08-01T10:22:44.525988Z",
     "shell.execute_reply.started": "2022-08-01T10:22:12.270369Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   12.2s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   27.4s finished\n",
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "# Обучаем модель на тестовом наборе данных\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Используем обученную модель для предсказания рейтинга ресторанов в тестовой выборке.\n",
    "# Предсказанные значения записываем в переменную y_pred\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:44.529202Z",
     "iopub.status.busy": "2022-08-01T10:22:44.528901Z",
     "iopub.status.idle": "2022-08-01T10:22:44.536257Z",
     "shell.execute_reply": "2022-08-01T10:22:44.535004Z",
     "shell.execute_reply.started": "2022-08-01T10:22:44.529154Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.17431124999999997\n"
     ]
    }
   ],
   "source": [
    "# Сравниваем предсказанные значения (y_pred) с реальными (y_test), и смотрим насколько они в среднем отличаются\n",
    "# Метрика называется Mean Absolute Error (MAE) и показывает среднее отклонение предсказанных значений от фактических.\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:22:44.538154Z",
     "iopub.status.busy": "2022-08-01T10:22:44.537836Z",
     "iopub.status.idle": "2022-08-01T10:22:44.857960Z",
     "shell.execute_reply": "2022-08-01T10:22:44.856564Z",
     "shell.execute_reply.started": "2022-08-01T10:22:44.538095Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.1414375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "# Округлим результаты работы модели:\n",
    "def round_to_polovina(row):\n",
    "    return (round(row*2.0)/2)\n",
    "\n",
    "new_round = np.vectorize(round_to_polovina)\n",
    "y_pred_round = new_round(model.predict(X_test))\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, y_pred_round))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:23:00.271919Z",
     "iopub.status.busy": "2022-08-01T10:23:00.271560Z",
     "iopub.status.idle": "2022-08-01T10:23:00.762036Z",
     "shell.execute_reply": "2022-08-01T10:23:00.760840Z",
     "shell.execute_reply.started": "2022-08-01T10:23:00.271865Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdec147c0b8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# в RandomForestRegressor есть возможность вывести самые важные признаки для модели\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(15).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission\n",
    "Если все устраевает - готовим Submission на кагл"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_data = test_data.drop(['Rating'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict_submission = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample_submission['Rating'] = predict_submission\n",
    "# sample_submission.to_csv('submission.csv', index=False)\n",
    "# sample_submission.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-08-01T10:23:40.701892Z",
     "iopub.status.busy": "2022-08-01T10:23:40.701499Z",
     "iopub.status.idle": "2022-08-01T10:23:41.754089Z",
     "shell.execute_reply": "2022-08-01T10:23:41.753119Z",
     "shell.execute_reply.started": "2022-08-01T10:23:40.701835Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "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>Restaurant_id</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3441</th>\n",
       "      <td>id_1423</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5093</th>\n",
       "      <td>id_4337</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5553</th>\n",
       "      <td>id_4323</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6034</th>\n",
       "      <td>id_7376</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11346</th>\n",
       "      <td>id_704</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>id_9995</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>id_9996</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>id_9997</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>id_9998</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>id_9999</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Restaurant_id  Rating\n",
       "3441        id_1423     4.0\n",
       "5093        id_4337     3.5\n",
       "5553        id_4323     4.0\n",
       "6034        id_7376     5.0\n",
       "11346        id_704     3.5\n",
       "...             ...     ...\n",
       "49995       id_9995     4.5\n",
       "49996       id_9996     4.0\n",
       "49997       id_9997     3.0\n",
       "49998       id_9998     4.5\n",
       "49999       id_9999     4.0\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Используем обученную модель для предсказания рейтинга ресторанов в тестовой выборке.\n",
    "# Предсказанные значения записываем в переменную y_pred\n",
    "kaggle_df = df_for_test[~df_for_test['Main']]\n",
    "y_pred = model.predict(kaggle_df)\n",
    "\n",
    "submission = pd.DataFrame({\n",
    "        \"Restaurant_id\": df[~df['Main']]['Restaurant_id'],\n",
    "        \"Rating\": y_pred\n",
    "    })\n",
    "#submission['Restaurant_id']=submission['Restaurant_id'].astype('str')\n",
    "\n",
    "submission['Rating'] = submission['Rating'].apply(round_to_polovina)\n",
    "# submission.to_csv('/kaggle/working/submission.csv', index=False)\n",
    "submission.to_csv('submission.csv', index=False)\n",
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What's next?\n",
    "Или что делать, чтоб улучшить результат:\n",
    "* Обработать оставшиеся признаки в понятный для машины формат\n",
    "* Посмотреть, что еще можно извлечь из признаков\n",
    "* Сгенерировать новые признаки\n",
    "* Подгрузить дополнительные данные, например: по населению или благосостоянию городов\n",
    "* Подобрать состав признаков\n",
    "\n",
    "В общем, процесс творческий и весьма увлекательный! Удачи в соревновании!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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