EDA_Project_3_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel_address</th>\n",
       "      <th>additional_number_of_scoring</th>\n",
       "      <th>review_date</th>\n",
       "      <th>average_score</th>\n",
       "      <th>hotel_name</th>\n",
       "      <th>reviewer_nationality</th>\n",
       "      <th>negative_review</th>\n",
       "      <th>review_total_negative_word_counts</th>\n",
       "      <th>total_number_of_reviews</th>\n",
       "      <th>positive_review</th>\n",
       "      <th>review_total_positive_word_counts</th>\n",
       "      <th>total_number_of_reviews_reviewer_has_given</th>\n",
       "      <th>reviewer_score</th>\n",
       "      <th>tags</th>\n",
       "      <th>days_since_review</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stratton Street Mayfair Westminster Borough Lo...</td>\n",
       "      <td>581</td>\n",
       "      <td>2/19/2016</td>\n",
       "      <td>8.4</td>\n",
       "      <td>The May Fair Hotel</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Leaving</td>\n",
       "      <td>3</td>\n",
       "      <td>1994</td>\n",
       "      <td>Staff were amazing</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>[' Leisure trip ', ' Couple ', ' Studio Suite ...</td>\n",
       "      <td>531 day</td>\n",
       "      <td>51.507894</td>\n",
       "      <td>-0.143671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>130 134 Southampton Row Camden London WC1B 5AF...</td>\n",
       "      <td>299</td>\n",
       "      <td>1/12/2017</td>\n",
       "      <td>8.3</td>\n",
       "      <td>Mercure London Bloomsbury Hotel</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>poor breakfast</td>\n",
       "      <td>3</td>\n",
       "      <td>1361</td>\n",
       "      <td>location</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>6.3</td>\n",
       "      <td>[' Business trip ', ' Couple ', ' Standard Dou...</td>\n",
       "      <td>203 day</td>\n",
       "      <td>51.521009</td>\n",
       "      <td>-0.123097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151 bis Rue de Rennes 6th arr 75006 Paris France</td>\n",
       "      <td>32</td>\n",
       "      <td>10/18/2016</td>\n",
       "      <td>8.9</td>\n",
       "      <td>Legend Saint Germain by Elegancia</td>\n",
       "      <td>China</td>\n",
       "      <td>No kettle in room</td>\n",
       "      <td>6</td>\n",
       "      <td>406</td>\n",
       "      <td>No Positive</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>7.5</td>\n",
       "      <td>[' Leisure trip ', ' Solo traveler ', ' Modern...</td>\n",
       "      <td>289 day</td>\n",
       "      <td>48.845377</td>\n",
       "      <td>2.325643</td>\n",
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      "text/plain": [
       "                                       hotel_address  \\\n",
       "0  Stratton Street Mayfair Westminster Borough Lo...   \n",
       "1  130 134 Southampton Row Camden London WC1B 5AF...   \n",
       "2   151 bis Rue de Rennes 6th arr 75006 Paris France   \n",
       "\n",
       "   additional_number_of_scoring review_date  average_score  \\\n",
       "0                           581   2/19/2016            8.4   \n",
       "1                           299   1/12/2017            8.3   \n",
       "2                            32  10/18/2016            8.9   \n",
       "\n",
       "                          hotel_name reviewer_nationality  \\\n",
       "0                 The May Fair Hotel      United Kingdom    \n",
       "1    Mercure London Bloomsbury Hotel      United Kingdom    \n",
       "2  Legend Saint Germain by Elegancia               China    \n",
       "\n",
       "       negative_review  review_total_negative_word_counts  \\\n",
       "0             Leaving                                   3   \n",
       "1       poor breakfast                                  3   \n",
       "2   No kettle in room                                   6   \n",
       "\n",
       "   total_number_of_reviews      positive_review  \\\n",
       "0                     1994   Staff were amazing   \n",
       "1                     1361             location   \n",
       "2                      406          No Positive   \n",
       "\n",
       "   review_total_positive_word_counts  \\\n",
       "0                                  4   \n",
       "1                                  2   \n",
       "2                                  0   \n",
       "\n",
       "   total_number_of_reviews_reviewer_has_given  reviewer_score  \\\n",
       "0                                           7            10.0   \n",
       "1                                          14             6.3   \n",
       "2                                          14             7.5   \n",
       "\n",
       "                                                tags days_since_review  \\\n",
       "0  [' Leisure trip ', ' Couple ', ' Studio Suite ...           531 day   \n",
       "1  [' Business trip ', ' Couple ', ' Standard Dou...           203 day   \n",
       "2  [' Leisure trip ', ' Solo traveler ', ' Modern...           289 day   \n",
       "\n",
       "         lat       lng  \n",
       "0  51.507894 -0.143671  \n",
       "1  51.521009 -0.123097  \n",
       "2  48.845377  2.325643  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hotels = pd.read_csv('hotels.csv')\n",
    "hotels.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Разбиваем датафрейм на части, необходимые для обучения и тестирования модели  \n",
    "# Х - данные с информацией об отелях, у - целевая переменная (рейтинги отелей)  \n",
    "X = hotels.drop(['reviewer_score'], axis = 1)  \n",
    "y = hotels['reviewer_score'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/home/anna/snap/jupyter/common/lib/python3.7/site-packages/joblib/_multiprocessing_helpers.py:45: UserWarning: [Errno 13] Permission denied.  joblib will operate in serial mode\n",
      "  warnings.warn('%s.  joblib will operate in serial mode' % (e,))\n"
     ]
    }
   ],
   "source": [
    "# Загружаем специальный инструмент для разбивки:  \n",
    "from sklearn.model_selection import train_test_split  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Наборы данных с меткой \"train\" будут использоваться для обучения модели, \"test\" - для тестирования.  \n",
    "# Для тестирования мы будем использовать 25% от исходного датасета.  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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  \n",
    "  \n",
    "# Создаём модель  \n",
    "regr = RandomForestRegressor(n_estimators=100)  \n",
    "      \n",
    "# Обучаем модель на тестовом наборе данных  \n",
    "regr.fit(X_train, y_train)  \n",
    "      \n",
    "# Используем обученную модель для предсказания рейтинга отелей в тестовой выборке.  \n",
    "# Предсказанные значения записываем в переменную y_pred  \n",
    "y_pred = regr.predict(X_test)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 0.14138286324223787\n"
     ]
    }
   ],
   "source": [
    "# Сравниваем предсказанные значения (y_pred) с реальными (y_test), и смотрим насколько они отличаются  \n",
    "# Метрика называется Mean Absolute Percentage Error (MAPE) и показывает среднюю абсолютную процентную ошибку предсказанных значений от фактических.  \n",
    "print('MAPE:', metrics.mean_absolute_percentage_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Небольшой бонус:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 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 hotels.columns if hotels[s].dtypes == 'object']\n",
    "# hotels.drop(object_columns, axis = 1, inplace=True)\n",
    "\n",
    "# # заполняем пропуски самым простым способом\n",
    "# hotels = hotels.fillna(0)"
   ]
  }
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