notebook.ipynb

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   "source": [
    "# **Zomato Restaurants Ratings**\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#assessment\">Data Assessment</a></li>\n",
    "<li><a href=\"#cleaning\">Data Cleaning</a></li>\n",
    "<li><a href=\"#explore\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#model\">Model Building</a></li>\n",
    "<li><a href=\"#conculsion\">Conculsion</a></li>\n",
    "<li><a href=\"#limitation\">Limitations</a></li>\n",
    "</ul>\n",
    "\n",
    "### INTRODUCTION\n",
    "#### Objectives\n",
    "The objective of this project is to build machine learning approach to predict the rating of a restaurant in Bangalore.\n",
    "\n",
    "#### About Dataset\n",
    "The data is accurate to that available on the zomato website until 15 March 2019.\n",
    "The data was scraped from Zomato in two phase. After going through the structure of the website I found that for each neighborhood there are 6-7 category of restaurants viz. Buffet, Cafes, Delivery, Desserts, Dine-out, Drinks & nightlife, Pubs and bars.\n",
    "\n",
    "Phase I,\n",
    "\n",
    "In Phase I of extraction only the URL, name and address of the restaurant were extracted which were visible on the front page. The URl's for each of the restaurants on the zomato were recorded in the csv file so that later the data can be extracted individually for each restaurant. This made the extraction process easier and reduced the extra load on my machine. The data for each neighborhood and each category can be found here\n",
    "\n",
    "Phase II,\n",
    "\n",
    "In Phase II the recorded data for each restaurant and each category was read and data for each restaurant was scraped individually. 15 variables were scraped in this phase. For each of the neighborhood and for each category their onlineorder, booktable, rate, votes, phone, location, resttype, dishliked, cuisines, approxcost(for two people), reviewslist, menu_item was extracted.\n",
    "\n",
    "Data Source: https://www.kaggle.com/datasets/himanshupoddar/zomato-bangalore-restaurants\n",
    "\n",
    "Columns Description: The following are the description of each columns in the dataset:\n",
    "- url: zomato url for the restaurants\n",
    "- address: complete location of the restaurant\n",
    "- name: name of the restaurant\n",
    "- online_order: whether restaurant accepts online order\n",
    "- book_table: whether restaurant provides option for booking table\n",
    "- rate: restaurants rating on zomato website\n",
    "- votes: number of individual who voted for restaurants\n",
    "- phone: contact details of the restaurant\n",
    "- localtion: area where restaurant is situated\n",
    "- rest_type: Type of restaurants (Categorical value)\n",
    "- dish_liked: what are all dishes of the restaurant that people liked\n",
    "- cuisines: cuisines offered by the restaurant\n",
    "- approx_cost(for two people): average cost for two people\n",
    "- review_list: reviews of the restaurant on zomato website\n",
    "- menu_item: menu items available in the restuarant\n",
    "- listed_in(type): type of the restaurant\n",
    "- listed_in(city): locality of the restaurant position"
   ]
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   "source": [
    "### Preliminary Wrangling"
   ]
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   "source": [
    "#IMPORT THE NECESSARY LIBRARIES\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import plotly.express as px\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f5414be-c441-4ab8-b23c-710121fe7a90",
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    "id": "1f5414be-c441-4ab8-b23c-710121fe7a90",
    "outputId": "2b588269-0817-4b82-d22b-65b45179216e"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('zomato.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0d36b7e-5bda-4f72-9285-f5f456b552e2",
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>url</th>\n",
       "      <th>address</th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>phone</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>reviews_list</th>\n",
       "      <th>menu_item</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.zomato.com/bangalore/jalsa-banasha...</td>\n",
       "      <td>942, 21st Main Road, 2nd Stage, Banashankari, ...</td>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>080 42297555\\r\\n+91 9743772233</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  A beautiful place to ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.zomato.com/bangalore/spice-elephan...</td>\n",
       "      <td>2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...</td>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>080 41714161</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Had been here for din...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.zomato.com/SanchurroBangalore?cont...</td>\n",
       "      <td>1112, Next to KIMS Medical College, 17th Cross...</td>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>+91 9663487993</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>[('Rated 3.0', \"RATED\\n  Ambience is not that ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.zomato.com/bangalore/addhuri-udupi...</td>\n",
       "      <td>1st Floor, Annakuteera, 3rd Stage, Banashankar...</td>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>+91 9620009302</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>[('Rated 4.0', \"RATED\\n  Great food and proper...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.zomato.com/bangalore/grand-village...</td>\n",
       "      <td>10, 3rd Floor, Lakshmi Associates, Gandhi Baza...</td>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>+91 8026612447\\r\\n+91 9901210005</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Very good restaurant ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51712</th>\n",
       "      <td>https://www.zomato.com/bangalore/best-brews-fo...</td>\n",
       "      <td>Four Points by Sheraton Bengaluru, 43/3, White...</td>\n",
       "      <td>Best Brews - Four Points by Sheraton Bengaluru...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.6 /5</td>\n",
       "      <td>27</td>\n",
       "      <td>080 40301477</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Continental</td>\n",
       "      <td>1,500</td>\n",
       "      <td>[('Rated 5.0', \"RATED\\n  Food and service are ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51713</th>\n",
       "      <td>https://www.zomato.com/bangalore/vinod-bar-and...</td>\n",
       "      <td>Number 10, Garudachar Palya, Mahadevapura, Whi...</td>\n",
       "      <td>Vinod Bar And Restaurant</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>+91 8197675843</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>600</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51714</th>\n",
       "      <td>https://www.zomato.com/bangalore/plunge-sherat...</td>\n",
       "      <td>Sheraton Grand Bengaluru Whitefield Hotel &amp; Co...</td>\n",
       "      <td>Plunge - Sheraton Grand Bengaluru Whitefield H...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2,000</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51715</th>\n",
       "      <td>https://www.zomato.com/bangalore/chime-sherato...</td>\n",
       "      <td>Sheraton Grand Bengaluru Whitefield Hotel &amp; Co...</td>\n",
       "      <td>Chime - Sheraton Grand Bengaluru Whitefield Ho...</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.3 /5</td>\n",
       "      <td>236</td>\n",
       "      <td>080 49652769</td>\n",
       "      <td>ITPL Main Road, Whitefield</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Cocktails, Pizza, Buttermilk</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>2,500</td>\n",
       "      <td>[('Rated 4.0', 'RATED\\n  Nice and friendly pla...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51716</th>\n",
       "      <td>https://www.zomato.com/bangalore/the-nest-the-...</td>\n",
       "      <td>ITPL Main Road, KIADB Export Promotion Industr...</td>\n",
       "      <td>The Nest - The Den Bengaluru</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.4 /5</td>\n",
       "      <td>13</td>\n",
       "      <td>+91 8071117272</td>\n",
       "      <td>ITPL Main Road, Whitefield</td>\n",
       "      <td>Bar, Casual Dining</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finger Food, North Indian, Continental</td>\n",
       "      <td>1,500</td>\n",
       "      <td>[('Rated 5.0', 'RATED\\n  Great ambience , look...</td>\n",
       "      <td>[]</td>\n",
       "      <td>Pubs and bars</td>\n",
       "      <td>Whitefield</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51717 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url  \\\n",
       "0      https://www.zomato.com/bangalore/jalsa-banasha...   \n",
       "1      https://www.zomato.com/bangalore/spice-elephan...   \n",
       "2      https://www.zomato.com/SanchurroBangalore?cont...   \n",
       "3      https://www.zomato.com/bangalore/addhuri-udupi...   \n",
       "4      https://www.zomato.com/bangalore/grand-village...   \n",
       "...                                                  ...   \n",
       "51712  https://www.zomato.com/bangalore/best-brews-fo...   \n",
       "51713  https://www.zomato.com/bangalore/vinod-bar-and...   \n",
       "51714  https://www.zomato.com/bangalore/plunge-sherat...   \n",
       "51715  https://www.zomato.com/bangalore/chime-sherato...   \n",
       "51716  https://www.zomato.com/bangalore/the-nest-the-...   \n",
       "\n",
       "                                                 address  \\\n",
       "0      942, 21st Main Road, 2nd Stage, Banashankari, ...   \n",
       "1      2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...   \n",
       "2      1112, Next to KIMS Medical College, 17th Cross...   \n",
       "3      1st Floor, Annakuteera, 3rd Stage, Banashankar...   \n",
       "4      10, 3rd Floor, Lakshmi Associates, Gandhi Baza...   \n",
       "...                                                  ...   \n",
       "51712  Four Points by Sheraton Bengaluru, 43/3, White...   \n",
       "51713  Number 10, Garudachar Palya, Mahadevapura, Whi...   \n",
       "51714  Sheraton Grand Bengaluru Whitefield Hotel & Co...   \n",
       "51715  Sheraton Grand Bengaluru Whitefield Hotel & Co...   \n",
       "51716  ITPL Main Road, KIADB Export Promotion Industr...   \n",
       "\n",
       "                                                    name online_order  \\\n",
       "0                                                  Jalsa          Yes   \n",
       "1                                         Spice Elephant          Yes   \n",
       "2                                        San Churro Cafe          Yes   \n",
       "3                                  Addhuri Udupi Bhojana           No   \n",
       "4                                          Grand Village           No   \n",
       "...                                                  ...          ...   \n",
       "51712  Best Brews - Four Points by Sheraton Bengaluru...           No   \n",
       "51713                           Vinod Bar And Restaurant           No   \n",
       "51714  Plunge - Sheraton Grand Bengaluru Whitefield H...           No   \n",
       "51715  Chime - Sheraton Grand Bengaluru Whitefield Ho...           No   \n",
       "51716                       The Nest - The Den Bengaluru           No   \n",
       "\n",
       "      book_table    rate  votes                             phone  \\\n",
       "0            Yes   4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1             No   4.1/5    787                      080 41714161   \n",
       "2             No   3.8/5    918                    +91 9663487993   \n",
       "3             No   3.7/5     88                    +91 9620009302   \n",
       "4             No   3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \n",
       "...          ...     ...    ...                               ...   \n",
       "51712         No  3.6 /5     27                      080 40301477   \n",
       "51713         No     NaN      0                    +91 8197675843   \n",
       "51714         No     NaN      0                               NaN   \n",
       "51715        Yes  4.3 /5    236                      080 49652769   \n",
       "51716         No  3.4 /5     13                    +91 8071117272   \n",
       "\n",
       "                         location            rest_type  \\\n",
       "0                    Banashankari        Casual Dining   \n",
       "1                    Banashankari        Casual Dining   \n",
       "2                    Banashankari  Cafe, Casual Dining   \n",
       "3                    Banashankari          Quick Bites   \n",
       "4                    Basavanagudi        Casual Dining   \n",
       "...                           ...                  ...   \n",
       "51712                  Whitefield                  Bar   \n",
       "51713                  Whitefield                  Bar   \n",
       "51714                  Whitefield                  Bar   \n",
       "51715  ITPL Main Road, Whitefield                  Bar   \n",
       "51716  ITPL Main Road, Whitefield   Bar, Casual Dining   \n",
       "\n",
       "                                              dish_liked  \\\n",
       "0      Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1      Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2      Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3                                            Masala Dosa   \n",
       "4                                    Panipuri, Gol Gappe   \n",
       "...                                                  ...   \n",
       "51712                                                NaN   \n",
       "51713                                                NaN   \n",
       "51714                                                NaN   \n",
       "51715                       Cocktails, Pizza, Buttermilk   \n",
       "51716                                                NaN   \n",
       "\n",
       "                                     cuisines approx_cost(for two people)  \\\n",
       "0              North Indian, Mughlai, Chinese                         800   \n",
       "1                 Chinese, North Indian, Thai                         800   \n",
       "2                      Cafe, Mexican, Italian                         800   \n",
       "3                  South Indian, North Indian                         300   \n",
       "4                    North Indian, Rajasthani                         600   \n",
       "...                                       ...                         ...   \n",
       "51712                             Continental                       1,500   \n",
       "51713                             Finger Food                         600   \n",
       "51714                             Finger Food                       2,000   \n",
       "51715                             Finger Food                       2,500   \n",
       "51716  Finger Food, North Indian, Continental                       1,500   \n",
       "\n",
       "                                            reviews_list menu_item  \\\n",
       "0      [('Rated 4.0', 'RATED\\n  A beautiful place to ...        []   \n",
       "1      [('Rated 4.0', 'RATED\\n  Had been here for din...        []   \n",
       "2      [('Rated 3.0', \"RATED\\n  Ambience is not that ...        []   \n",
       "3      [('Rated 4.0', \"RATED\\n  Great food and proper...        []   \n",
       "4      [('Rated 4.0', 'RATED\\n  Very good restaurant ...        []   \n",
       "...                                                  ...       ...   \n",
       "51712  [('Rated 5.0', \"RATED\\n  Food and service are ...        []   \n",
       "51713                                                 []        []   \n",
       "51714                                                 []        []   \n",
       "51715  [('Rated 4.0', 'RATED\\n  Nice and friendly pla...        []   \n",
       "51716  [('Rated 5.0', 'RATED\\n  Great ambience , look...        []   \n",
       "\n",
       "      listed_in(type) listed_in(city)  \n",
       "0              Buffet    Banashankari  \n",
       "1              Buffet    Banashankari  \n",
       "2              Buffet    Banashankari  \n",
       "3              Buffet    Banashankari  \n",
       "4              Buffet    Banashankari  \n",
       "...               ...             ...  \n",
       "51712   Pubs and bars      Whitefield  \n",
       "51713   Pubs and bars      Whitefield  \n",
       "51714   Pubs and bars      Whitefield  \n",
       "51715   Pubs and bars      Whitefield  \n",
       "51716   Pubs and bars      Whitefield  \n",
       "\n",
       "[51717 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176c555c-8dfd-4c8f-a457-50f8d27ba194",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='assessment'></a>\n",
    "### Data Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c256ad29-5efb-4340-a4c6-8a5b8d552df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51717 entries, 0 to 51716\n",
      "Data columns (total 17 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   url                          51717 non-null  object\n",
      " 1   address                      51717 non-null  object\n",
      " 2   name                         51717 non-null  object\n",
      " 3   online_order                 51717 non-null  object\n",
      " 4   book_table                   51717 non-null  object\n",
      " 5   rate                         43942 non-null  object\n",
      " 6   votes                        51717 non-null  int64 \n",
      " 7   phone                        50509 non-null  object\n",
      " 8   location                     51696 non-null  object\n",
      " 9   rest_type                    51490 non-null  object\n",
      " 10  dish_liked                   23639 non-null  object\n",
      " 11  cuisines                     51672 non-null  object\n",
      " 12  approx_cost(for two people)  51371 non-null  object\n",
      " 13  reviews_list                 51717 non-null  object\n",
      " 14  menu_item                    51717 non-null  object\n",
      " 15  listed_in(type)              51717 non-null  object\n",
      " 16  listed_in(city)              51717 non-null  object\n",
      "dtypes: int64(1), object(16)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc2c1eda-b548-4efe-b746-2d09423095fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                                0\n",
       "address                            0\n",
       "name                               0\n",
       "online_order                       0\n",
       "book_table                         0\n",
       "rate                            7775\n",
       "votes                              0\n",
       "phone                           1208\n",
       "location                          21\n",
       "rest_type                        227\n",
       "dish_liked                     28078\n",
       "cuisines                          45\n",
       "approx_cost(for two people)      346\n",
       "reviews_list                       0\n",
       "menu_item                          0\n",
       "listed_in(type)                    0\n",
       "listed_in(city)                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4355cff1-fd80-428a-82fc-944c163c2523",
   "metadata": {
    "id": "4355cff1-fd80-428a-82fc-944c163c2523",
    "outputId": "67ee3e73-b281-4239-989a-5f1cb268c8a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',\n",
       "       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',\n",
       "       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',\n",
       "       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',\n",
       "       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',\n",
       "       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',\n",
       "       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',\n",
       "       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',\n",
       "       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',\n",
       "       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading unique values from 'rate' column\n",
    "df['rate'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f4a3b38-8cb9-4cb9-b02e-b918ca0f5539",
   "metadata": {},
   "source": [
    "#### Assessment Results\n",
    "- There are duplicate rows\n",
    "- Unnecessary columns: `url, address, phone, dish_liked, reviews_list, menu_item, listed_in(city)`\n",
    "- Unwanted values in the rate column ie `'NEW', '-'`\n",
    "- The columns `rate, location, rest_type, cuisines, approx_cost(for two people)` has null values\n",
    "- Rename columns: `approx_cost(for two people)` to `cost_for_2`, `listed_in(type)` to `type`, `listed_in(city)` to `city`\n",
    "- The columns `rate`, `approx_cost(for two people)`  should be of datatype `float`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1f79ba-92d7-44f7-8660-2b1da64d8ed3",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='cleaning'></a>\n",
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18b20dc-11ea-4b97-a164-e8967b9a75d3",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Drop unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b0aeb82-92fc-4cd6-97a1-38213751ae29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Addhuri Udupi Bhojana</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Masala Dosa</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grand Village</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>Basavanagudi</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Panipuri, Gol Gappe</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name online_order book_table   rate  votes      location  \\\n",
       "0                  Jalsa          Yes        Yes  4.1/5    775  Banashankari   \n",
       "1         Spice Elephant          Yes         No  4.1/5    787  Banashankari   \n",
       "2        San Churro Cafe          Yes         No  3.8/5    918  Banashankari   \n",
       "3  Addhuri Udupi Bhojana           No         No  3.7/5     88  Banashankari   \n",
       "4          Grand Village           No         No  3.8/5    166  Basavanagudi   \n",
       "\n",
       "             rest_type                                         dish_liked  \\\n",
       "0        Casual Dining  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1        Casual Dining  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Cafe, Casual Dining  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "3          Quick Bites                                        Masala Dosa   \n",
       "4        Casual Dining                                Panipuri, Gol Gappe   \n",
       "\n",
       "                         cuisines approx_cost(for two people) listed_in(type)  \n",
       "0  North Indian, Mughlai, Chinese                         800          Buffet  \n",
       "1     Chinese, North Indian, Thai                         800          Buffet  \n",
       "2          Cafe, Mexican, Italian                         800          Buffet  \n",
       "3      South Indian, North Indian                         300          Buffet  \n",
       "4        North Indian, Rajasthani                         600          Buffet  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unwanted_columns = ['url', 'address', 'phone', 'reviews_list', 'menu_item', 'listed_in(city)']\n",
    "df.drop(unwanted_columns, axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15fff982-2a86-4df8-b4f4-72d62d7cc6e9",
   "metadata": {},
   "source": [
    "##### Replace 'NEW' and '-' in column `rate` to 'NaN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5588fe9c-0bac-477e-825a-7e05ae7530db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rate'].replace(['NEW', '-'], np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842486e1-fb72-4b75-bd8f-ec2bcc5caef9",
   "metadata": {},
   "source": [
    "##### Drop null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a4e3d99-db7d-4e26-9732-e3b37aead14e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                           0\n",
       "online_order                   0\n",
       "book_table                     0\n",
       "rate                           0\n",
       "votes                          0\n",
       "location                       0\n",
       "rest_type                      0\n",
       "dish_liked                     0\n",
       "cuisines                       0\n",
       "approx_cost(for two people)    0\n",
       "listed_in(type)                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b0fb27-b97a-4e8d-9bb2-5e3440c5ebdc",
   "metadata": {},
   "source": [
    "##### Rename columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5dea00a8-6ebc-4d04-88d9-b5ef5927f696",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'online_order', 'book_table', 'rate', 'votes', 'location',\n",
       "       'rest_type', 'dish_liked', 'cuisines', 'cost_for_2', 'type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'approx_cost(for two people)':'cost_for_2',\n",
    "                     'listed_in(type)':'type',\n",
    "                     'listed_in(city)':'city'}, inplace=True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "076b9231-021f-40b1-b378-f24451fdfbb4",
   "metadata": {},
   "source": [
    "##### Convert the columns `rate, cost_for_2` to datatype `float`\n",
    ">   \n",
    "     We remove '/5' from values in the rate column and then convert to float.\n",
    "     We remove the ',' from values in the cost_for_2 column and then convert to float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "203aedc5-dcde-4a15-a84e-654c1ae637bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing '/5' from rate\n",
    "df['rate'] = df['rate'].apply(lambda x: x.replace('/5', '') if isinstance(x, str) and '/5' in x else x).astype(float)\n",
    "df['rate'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e2af312-ae91-4723-9c71-0878936773b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing ',' with empty string '' and converting to float\n",
    "df['cost_for_2'] = df['cost_for_2'].apply(lambda x: x.replace(',', '') if isinstance(x, str) and ',' in x else x).astype(float)\n",
    "df['cost_for_2'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1bbe68ed-a683-4131-a1be-4e3f9d70818c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         800.0\n",
       "1         800.0\n",
       "2         800.0\n",
       "3         300.0\n",
       "4         600.0\n",
       "          ...  \n",
       "23254    1200.0\n",
       "23255    2000.0\n",
       "23256    1200.0\n",
       "23257     800.0\n",
       "23258    2500.0\n",
       "Name: cost_for_2, Length: 23259, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cost_for_2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a63d553e-4baf-4ce3-874c-b6f7bf492398",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save Cleaned Data\n",
    "df.to_csv('zomato_clean.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecde0e50-7777-4f59-874a-02db53063d67",
   "metadata": {
    "id": "ecde0e50-7777-4f59-874a-02db53063d67",
    "tags": []
   },
   "source": [
    "<a id='explore'></a>\n",
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f85e574-91a6-4e2e-a1d8-3eba46b2487d",
   "metadata": {
    "id": "7f85e574-91a6-4e2e-a1d8-3eba46b2487d",
    "tags": []
   },
   "source": [
    "#### Most famous restaurants chain in Bangalore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aec85099-06bb-48ba-9bc2-0026fa7c2555",
   "metadata": {
    "id": "aec85099-06bb-48ba-9bc2-0026fa7c2555",
    "outputId": "53c7ca73-3906-423a-8865-b24a04557782"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "famous_count = df['name'].value_counts()[:20]\n",
    "fig = px.bar(famous_count,\n",
    "             x=\"name\",\n",
    "             y=famous_count.index,\n",
    "             color=famous_count.index,\n",
    "             template = 'ggplot2',\n",
    "             height = 600,\n",
    "            width = 900\n",
    "            )\n",
    "\n",
    "fig.update_layout(\n",
    "    title = 'Top 20 Most Famous Restaurants',\n",
    "    xaxis_title = 'Number of Outlets',\n",
    "    yaxis_title = 'Names of Restaurants'\n",
    ")\n",
    "   \n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15215528-5b1f-4fab-bdd9-197fe8cc054e",
   "metadata": {
    "id": "15215528-5b1f-4fab-bdd9-197fe8cc054e"
   },
   "source": [
    "**Insight:** Cafe Coffee Day, Onesta and Empire Restaurant are the top 3 most popular restaurants in Bangalore"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1719a9cc-1bc2-4b48-b773-0ba6bf644feb",
   "metadata": {
    "id": "1719a9cc-1bc2-4b48-b773-0ba6bf644feb",
    "tags": []
   },
   "source": [
    "#### Table Booking \n",
    "> Percentage of restaurants that provides option for booking table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "041f9449-c29f-4097-971d-e189e17fffeb",
   "metadata": {
    "id": "041f9449-c29f-4097-971d-e189e17fffeb",
    "outputId": "9a9c29ea-582f-499d-d101-1693fe77d4c2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bk_table = df['book_table'].value_counts()\n",
    "fig = px.pie(bk_table,\n",
    "             values = bk_table,\n",
    "             title = 'Table Booking',\n",
    "             names = bk_table.index,\n",
    "             height = 400,\n",
    "             width = 400,\n",
    "             template = 'seaborn'\n",
    "            )\n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21d61692-ed54-4a5c-9620-ca3bb8c59153",
   "metadata": {
    "id": "21d61692-ed54-4a5c-9620-ca3bb8c59153"
   },
   "source": [
    "**Insight:** Most restaurant do not offer the option of booking a table."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7066d15d-bbbc-4b09-bbfb-bdc413dfe733",
   "metadata": {
    "id": "7066d15d-bbbc-4b09-bbfb-bdc413dfe733"
   },
   "source": [
    "#### Restaurants deliver online or Not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12c86dd5-928d-4425-82d4-886c42797c96",
   "metadata": {
    "id": "12c86dd5-928d-4425-82d4-886c42797c96",
    "outputId": "64d9eb2e-53a3-45bd-f138-e2567fe649ae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "online_del = df['online_order'].value_counts()\n",
    "fig = px.bar(online_del,\n",
    "             y='online_order',\n",
    "             x=online_del.index,\n",
    "             color=online_del.index,\n",
    "             template = 'plotly',\n",
    "             height = 500,\n",
    "            width = 700\n",
    "            )\n",
    "\n",
    "fig.update_layout(\n",
    "    title = 'Restaurants delivering online or Not',\n",
    "    yaxis_title = 'Number of Restaurants',\n",
    "    xaxis_title = 'online order'\n",
    ")\n",
    "   \n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b76ffc-507d-48ed-9356-af37076a646f",
   "metadata": {
    "id": "58b76ffc-507d-48ed-9356-af37076a646f"
   },
   "source": [
    "**Insight:** Most restaurant offers online order and delivery"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfaa903d-99d0-4406-96f0-febc04237d30",
   "metadata": {
    "id": "dfaa903d-99d0-4406-96f0-febc04237d30"
   },
   "source": [
    "#### Ratings Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ad54830-fa3e-4c34-887c-621acec72f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings = [df['rate'].values]\n",
    "group_labels = ['ratings']\n",
    "fig = ff.create_distplot(ratings, group_labels,\n",
    "                        bin_size= 0.2)\n",
    "\n",
    "fig.update_layout(\n",
    "    height = 1000,\n",
    "    width = 700,\n",
    "    title = 'Ratings Distribution',\n",
    "    yaxis_title = 'Number of Ratings',\n",
    "    xaxis_title = 'ratings'\n",
    ")\n",
    "\n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6fbf84b-6bac-486b-a3f9-8ec48156cea3",
   "metadata": {
    "id": "d6fbf84b-6bac-486b-a3f9-8ec48156cea3"
   },
   "source": [
    "**Insight:** Most ratings are between 3.0 and 4.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9b3b54-14f0-4044-82f2-b3cf86204e45",
   "metadata": {
    "id": "bd9b3b54-14f0-4044-82f2-b3cf86204e45"
   },
   "source": [
    "#### Service Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3186e085-cd7a-428c-95e7-d353e11e5ade",
   "metadata": {
    "id": "3186e085-cd7a-428c-95e7-d353e11e5ade",
    "outputId": "db8e0287-949e-4ac5-ebfd-cf098e7c962f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "service = df['type'].value_counts()\n",
    "fig = px.bar(service,\n",
    "             x='type',\n",
    "             y=service.index,\n",
    "             color= service.index,\n",
    "             template = 'ggplot2',\n",
    "             height = 500,\n",
    "            width = 700\n",
    "            )\n",
    "\n",
    "fig.update_layout(\n",
    "    title = 'Types of services',\n",
    "    xaxis_title = 'Count',\n",
    "    yaxis_title = 'service types'\n",
    ")\n",
    "   \n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e111d59-1285-4971-86af-76c376fdeb94",
   "metadata": {
    "id": "1e111d59-1285-4971-86af-76c376fdeb94"
   },
   "source": [
    "**Insight:** Here the two main service types are Delivery and Dine-out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a64a37b-f027-45b5-8b5c-152ef5c10745",
   "metadata": {
    "id": "5a64a37b-f027-45b5-8b5c-152ef5c10745"
   },
   "source": [
    "#### Distribution of charges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e950677-0585-4581-8659-b48c673170a5",
   "metadata": {
    "id": "7e950677-0585-4581-8659-b48c673170a5",
    "outputId": "61e61c4c-18c7-4343-ff45-c96ec15e3218"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost = [df['cost_for_2'].values]\n",
    "group_labels = ['cost_for_2']\n",
    "fig = ff.create_distplot(cost, group_labels,\n",
    "                        bin_size= 200)\n",
    "\n",
    "fig.update_layout(\n",
    "    height = 1000,\n",
    "    width = 700,\n",
    "    title = 'Cost for 2 People Charge Distribution',\n",
    "    xaxis_title = 'cost_for_2'\n",
    ")\n",
    "\n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accad453-ef1c-4e4e-860b-26eb858d658a",
   "metadata": {
    "id": "accad453-ef1c-4e4e-860b-26eb858d658a"
   },
   "source": [
    "**Insight:** Majority of restaurants charges less than 2000 for two people "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cec5dc-d1ce-40e7-b4d5-1362ed1d8459",
   "metadata": {
    "id": "55cec5dc-d1ce-40e7-b4d5-1362ed1d8459"
   },
   "source": [
    "#### Most Liked Dishes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c0c7b36f-0597-4f45-b9a5-b07f8ca88480",
   "metadata": {
    "id": "c0c7b36f-0597-4f45-b9a5-b07f8ca88480"
   },
   "outputs": [],
   "source": [
    "likes = [item.strip() for dish in df['dish_liked'] for item in dish.split(',') if dish]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a69eb24a-d383-4a1d-8d81-8d0dee8ece08",
   "metadata": {
    "id": "a69eb24a-d383-4a1d-8d81-8d0dee8ece08",
    "outputId": "b109c15e-9b97-4350-fa11-ae77a3608f46"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pasta         3352\n",
       "Burgers       2994\n",
       "Cocktails     2773\n",
       "Pizza         2664\n",
       "Biryani       2058\n",
       "Coffee        1985\n",
       "Mocktails     1854\n",
       "Sandwiches    1657\n",
       "Paratha       1530\n",
       "Salads        1389\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favorite_food = pd.Series(likes).value_counts()\n",
    "favorite_food.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "965ccd08-b5a2-49b4-a00d-94438978fd70",
   "metadata": {
    "id": "965ccd08-b5a2-49b4-a00d-94438978fd70",
    "outputId": "2ef60c00-2aad-4c68-f8f8-033acc58d422"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eMJRoAwEIQAACNhBAeG3IAjEgvA6uAYTXwaQRMgQgAIGcEnBVeFdWRK5+75YsLa2EylxDQ4O889ENov/Pyz4CCK99OakYEcJbERENIAABCEDAEgKuCu/i4pL8by/+Z/nq1ZuhSHZ3rZOh/T8n6zrCP/ktVMc0ioQAwhsJxmQ7QXiT5c1oEIAABCBQOwGXhff485fllW+9HmryPVsekt/65Z+vSXjN+fzmOFId0H98aagAAo30DP9Lly7JyMiItLW11dJFpq5BeB1MJ8LrYNIIGQIQgEBOCSC8lRPvfyDV3r17H3h4VeUeHmyB8K5mgvDWsopSvgbhTTkBDA8BCEAAAqEJILyVUQWFV6/Qh1JNTU15O7QXLlyQo0ePFjo6ffq07Ny50/vZ/9ArszP87LPPyoEDB0QfhKWvHTt2eA+v0odn+f+83l3kyjOzpwXCa08uQkdy9HP/r7z6/R+Fbk9DCEAAAhCAQFoEVHh/5aM/LY++9aFYQti0aV0s/WoNb9IlDb29vaI7vPoyO7THjh0TLXXYt2+fV5qgf37mzBlPYPXn4eFh6e/v9wRYxfns2bNeW5XkYEnDxYsXZdu2bdLT01MQ5ZMnTxbkORaQlnSK8FqSiGrC+Iuv3JC5+cVqLqFtRgg0SIO0tDTJ/AL5z0hKq55Gc3OTLC8ty7LeQs4rdwQaGxuksbFRVMZceempBY9saJOH37w2lpCzKLy6E6u7r319fQUBNvB0R3doaEjGxsaks7OzZLtKJQ3FdpVjSZAlnSK8liSimjDu3ZuXu3fnqrmEthkhoP/YbdjQITdv3snIjJhGtQTWr2/3Pv8LC+4IT7VzpH1pAq2tzdLa2iK3b98H008IZEl4/TetjY6OFmQ3WLawefNmmZycXLVTa8oXTLlDMeEtdnOcf5wsLyqE18HsIrwOJi2ikBHeiEA63A3C63DyIggd4X0QYpaE11/S4N/R1bpbU3rg3+HV0gT/S+t+x8fHvXKHL33pS6tKGozsdnV1yeDgYOHGuGJjRrBUresC4bUuJZUDQngrM8pqC4Q3q5kNPy+ENzyrLLZEePMpvKaEQQVXpVaFVXd4taRBa3X379/vgfELr4qxkV9tFyxhKFc2kcXPDsLrYFYRXgeTFlHICG9EIB3uBuF1OHkRhI7w5k94dca6u6u7tvravn279/9aw7tlyxbvpjVTCuEvdfCXL5hTGlSCBwYGvOu17caNG70b3syNchEsUWu7QHitTU3pwBBeB5MWUcgIb0QgHe4G4XU4eRGEjvBmU3gjWBp0UYEAwuvgEkF4HUxaRCEjvBGBdLgbhNfh5EUQOsKbnPAuLS3L/335urxxL9ypOG2tTfLUB94m7W08WjiCpR55Fwhv5Ejj7/Dvvv6KLM3Pxz8QI1hHoKFBfnws2Tx36FuXnIQCam5plOWlFVle5liyhJBbM8yKNEhrZ5d0PLKFUxp8WYnrpjUdYmVlRao5AVA3JXjZSQDhtTMvZaN67YXfkKW/edXByAkZAhCAAARqJtDSJh2/+Cl58zveg/AmJLw154oLrSOA8FqXksoBXX/+qCzNfqtyQ1pAAAIQgEBmCDSsaZeO3YMIbyCjce7wZmbxMBFBeB1cBAivg0kjZAhAAAJ1EkB4iwNEeOtcWDm5HOF1MNEIr4NJI2QIQAACdRJAeBHeOpdQri9HeB1MP8LrYNIIGQIQgECdBBBehLfOJZTryxFeB9OP8DqYNEKGAAQgUCcBhDdZ4dXTGd64cU1kOdyxZCsNjfLQ294uDXqcDi/rCCC81qWkckAIb2VGtIAABCCQNQIIb7LCu7i4JH/7H35f5l/7aqil1LSpW9760V+T1vaOUO1plCwBhDdZ3pGMhvBGgpFOIAABCDhFAOFNXnhnz47JwndfCbVOmt76mDzyz36jJuH1PwbYDOZ/THCoAGhUlgDC6+ACQXgdTBohQwACEKiTAMKbfeHt7e2VvXv3ehM9d+6cXLp0SUZGRqStra3O1cPluRRe179JIbx8cCEAAQjkjwDCmy/hvXz5soyPj8vExIR0dnbKyZMnPQCDg4Pe/+v7U1NTnhCr1xw8eFD27Nkjzz//vPf+5OSkd53++fT0dAHe6OhoQaq1j4GBAe+9HTt2FMa6evWqDA0NyUc+8hE5fvx44T3tU+PRl2s70LkWXle/SSG8+fuHjhlDAAIQQHjzJby6w3vt2rWC4IYR3q1btxZ2hM3mXldXl9eH+dm4j8qu/rlKbE9Pj7ejbMZT4T1w4IDs3r27qGDrjrNer6+dO3c68eFEeH/yqwOXvkkhvE58tggSAhCAQKQEEN7sC+/58+dXTVJ3Z82ObhjhPXz4cEFAzS7t2NiYJ7RB4Q32p+1PnDjh7SrfunXL2+E112pQKsRnzpwp7AJHurgT6Azh9dXKuPJNCuFN4JPBEBCAAAQsI4DwZl94/b95riSoxUoagsJrBFZLG4r1Z8oTDFlT1lBMeLWNSrK5RssnXKovzrXwuvpNCuG17F8hwoEABCCQAAEV3oc+cljW97xX5ubCnQ2bQFipD9HeviaWGPRYsqRPafALr9lVNZtxcezwdnd3F+p5/RCDu8NBwEF5jiUBEXeaa+F19ZsUwhvxp4DuIAABCDhAwAjvmx9T4V1wIOJkQuzoaI1loLSFNyiV/lMbdMLDw8PevP03rfl3eIPXm7rcQ4cOeZIbrOHVvl544QWvbrfYDu/Fixdl27ZtRcsjYklAxJ0ivD8paXDpmxTCG/GngO4gAAEIOECAkobiSdq0aV0s2UtDeIO/efafqOA/YUpPSPj4xz/unb5QSngVipHcmZkZeeqppzxOWhdsbjTzn9Jg3tOa4WI7vKXaxgI/hk4R3p8Ir0vfpBDeGD4JdAkBCEDAcgIIb3aFN4mlp7u2ugN85MgRb5c2b69cC6+r36QQ3rx9TJkvBCAAARGEN1nhXVpalltf+U+y8g9vhFt+a9pkw+M/Ly1r7XlIhJZBHD16tBD/6dOnnTlGLBz08K1yKbzh8dTeMs5vUghv7XnhSghAAAKuEkB4kxVeHW1lZUVWVsKvmMbGhvCNaZkoAYQ3QtxJfZNCeCNMGl1BAAIQcIQAwpu88DqyNAgzBAGENwQk25ogvLZlhHggAAEIxE8A4UV4419l2R0B4XUwtwivg0kjZAhAAAJ1EkB4Ed46l1CuL0d4HUw/wutg0ggZAhCAQJ0EEF6Et84llOvLEV4H04/wOpg0QoYABCBQJwGEF+Gtcwnl+nKE18H0I7wOJo2QIQABCNRJAOFNVnj1dIbv/N33ZGllOVTmGqRRet7yNmlo4KSGUMASboTwJgw8iuEQ3igo0gcEIAABtwggvMkKrz5p7dRf/oF87e++FWqhbH1oi/yLf/rPZV1be6j2STTSI1L1yWr+Rw7XOq7/6WtbtmzxHm3c29vrPabYhRfC60KWAjEivA4mjZAhAAEI1EkA4U1eeMf/7F/JV17/WqjMvePNW2X4qU/VJbz+RwGbQet5WEQ54a1WhhHeUMuARlESeO2F35Slv3k1yi7pCwIQgAAELCfQsKZN2n/xV+XNb3+33L593/Jokwtv06Z1sQymO7xJCu/ly5dlYGBA/IKrUjo5OSmHDh2Strbqn+AWpfD6Id+/f58d3lhWHZ2uIvB3X3tFlhbmoZJTAi0tTbKwsJTT2TPt5uYm0Uee6hOgeOWLwIo0yNqHH5H2t74N4fWlPgvCG1Yg/Q+42rNnj4yMjBRE2Mjt9PS0R2d0dFSefvrpVSUNps2uXbu8NhMTEwWS2l7LE06ePFn4882bN3vC3dPTI+V2eINjB2Oz4ZNKSYMNWagyhnv35uXu3bkqr6J5FgjoYys3bOiQmzfvZGE6zKEGAuvXt3uff7701AAvA5e0tjZLa2sLwpsx4fXLpMplsZfuAI+Pj3sy2tnZ6Ynp7OysJ70qzFqr29fX50mr/vzSSy+Jiq2p4X388ce9Xdn+/n7ZuXOnFNv91T+7cOGC7N+/3wvBP8aNGzdkaGhIxsbGJFjDq+26u7sL9bwvvPCC7N6924vTlhfCa0smqogD4a0CVsaaIrwZS2gN00F4a4CWoUsQ3geTmYUdXhXeEydOeEJbShJVKvU1ODjo/b9fklVU/TJsKBmp1ZKIP/7jP151k1mYGl6/ZGv7YsKrYqsi3dXVVYjNxo8cwmtjVirEhPA6mLSIQkZ4IwLpcDcIr8PJiyB0hDe7wmtkstQOb3AXVQVUT184cuSIt1s7NTW1qsRBSflLDXbs2FHYHfa/FzzBwdQSG9LmulLCqzvKxcopbDu9AeGN4C+gpLtAeJMmbs94CK89uUgrEoQ3LfJ2jIvwZlN4w9Tw1rPDq6UOjz766CopLrbDq7KrO8imbjfMDm9QbMOUZ6TxaUJ406Be55gIb50AHb4c4XU4eRGFjvBGBNLRbhDebAqvzqrSKQ1XrlxZJaPlanhNLa6WG/jP4dWb3i5duuTtBOsreJZusE5Y2585c8bbGS5X0nD27FnZt2+fdwMdwuvoXy42ho3w2piVZGJCeJPhbPMoCK/N2Yk/NoQ3u8KrM6t0Dm+5UxqC1xY7pUHH8IuySrQehaYvbW/qcc+fP+/92ZNPPim3b98uK7zBkx30unrODo7rU8QOb1xkY+z35e9ckTmOJYuRsL1d6xMr9Viy+XmOJbM3S/FG1tzSKMtLK7K8zLFk8ZKu3HtzQ7Ns2/COyg0jbIHwZlt4I1wqdBUggPA6uCT+1z/5Hfn2j77rYOSEDAEIQCA7BB57c7d8cscBadRvogm9EN7khFfPu37p6styZ+FuqOy2Na2V/+odPyvtrWtDtadRsgQQ3mR5RzLa8P9xUl794Xci6YtOIAABCECgNgLb1r9DfvX9v4zw1oYvsqviOpZMA9QHvFTzjBctO+NlJwGE1868lI0K4XUwaYQMAQhkjgDCa0dK4xReO2ZIFFEQQHijoJhwHwhvwsAZDgIQgEARAgivHcsC4bUjD7ZHgfDanqEi8SG8DiaNkCEAgcwRQHjtSCnCa0cebI8C4bU9QwivgxkiZAhAIA8EEF47sozw2pEH26NAeG3PEMLrYIYIGQIQyAMBhNeOLCO8duTB9igQXtszhPA6mCFChgAE8kAA4bUjy3EJr57O8MOr12RleTHcRBsaZcO2t0tDgsfUhQuMVkoA4a1hHQSfZ11DF94lwWdnh+2XGt5aiXMdBCAAgegIILzRsaynp7iEd3FxSb71O78vd7761VDhre3ulncN/pq0rusI1T5so6ArhL2OdqsJZF54zUIxj8nT6etzpQcHB2teC+XENKy0Irw14+dCCEAAAlYQQHitSIPEKbzfGB2TN155JdRE2x57TN77m79Rk/CWcxWENxT+io0yLby3bt3y5HbXrl2rBPfzn/+8PPPMM9LT01MRULEGUQlvsO+wsswOb01p4yIIQAACkRJAeCPFWXNnWRLe3t5e2bt3b+E3wF1dXXVt0NUMNYMXZlp4z507J5cuXZKRkRFpa2srmj5tc/ToUe+9PXv2rGprhHl6etp7f3R01FuIQTHVn19++WX5xCc+IZ/85CcL45j+Lly4UBhD3zx9+rTs3LmzYkmD9jsxMeH1t3nzZpmcnPQkHeHN4CeRKUEAAs4RQHjtSFkWhVfJXr58WaampuTYsWNy/PhxURnevXu3DA8Pi/+31sYr9P8HBgZWJcV4yI0bN+TAgQMyMzPjvV/vb7rtyHx1UWRWeMP8CkAX0/j4uCeVnZ2dnsjOzs560qvX64Lo6+srfNt66aWX5MMf/vAq4VVhvnbtWuEbWFCGtZ+zZ8/Kvn37POnW9mfOnPHG1J914ZpvdP5rzUI3sq4/60tFGeGtbpHTGgIQgEAcBBDeOKhW32dWhdds2vmFVzfd/K9SG3tmw+7w4cOeN1y8eFG2bdvmbZpdvXrVk191Dn0vL6/MC29/f3/JhAblVBfB0NCQjI2NiS4Wvwz7F4S5rru7+4Ed5EplCf4xtmzZUn6F86sAACAASURBVFJ4/WKsMu5/Ibx5+XgyTwhAwGYCKrxDvYekubkpsTCbmhqlublR5uZCnhyQWGTpDdTeviaWwfWmtaRreM0GmBFW3XQzu7rmPTNZ3QjT+5HMb3/Nn5sNv1LlEGE2BGMBmnKnmRfe4AIJiqtKq/nGpAtMvw0dOXLEE179VUKxcgh/qYEpTzD9FhNe823K/CrBlCeUE17tzz+Ov9wC4U35U8PwEIAABEREhfewCm9TY2I8VHabmppkbm4hsTFtH6ijozWWENMQXn+pgimjLCaowR3cSru+xW6KM/3HAs/CTjMrvMq6Ug1vPTu8Wvrw6U9/Wj7zmc94gmxugCvWp/9XB2F3eP1rJbjYEV4LP0mEBAEI5I4AJQ12pDyrJQ3BHdvgDW3FNvSCpZraR3DHlx1eO9ZtpFFUOqVB3/f/OqBcDa+21ZvP9u/fv6qG1y+wKr1ByQ6+7/8VRLkdXn+9DcIb6bKgMwhAAAKREEB4I8FYdyd5E96wdbulhNlfLhGsCa47GRZ3kOkdXv83m1Ln8JY7pSFYilDqlAa/xGq9rd7spic7mDKEU6dOFU5b2L59u7cctE64nPBqn/67Lf13VLLDa/EnitAgAIHcEEB47Uh1noT36aefLjiGn76WV16/fn3ViVD6vvGQK1euFJxCyyo3btwoeo8TwmvHGiaKEgQQXpYGBCAAgfQJILzp50AjyILw2kEy21Fkfoc3i+lDeLOYVeYEAQi4RgDhtSNjcQnv0tKy3Ph//pMs334j1EQb29rkkad+Xta0Fz/3P1QnNIqNAMIbG9r4OkZ442NLzxCAAATCEkB4w5KKt11cwqtRr6ysyMpK+PgbGxvCN6ZlogQQ3kRxRzMYwhsNR3qBAAQgUA8BhLceetFdG6fwRhclPaVNAOFNOwM1jI/w1gCNSyAAAQhETADhjRhojd0hvDWCy9llCK+DCUd4HUwaIUMAApkjgPDakVKE14482B4Fwmt7horEh/A6mDRChgAEMkcA4bUjpQivHXmwPQqE1/YMIbwOZoiQIQCBPBBAeO3IMsJrRx5sjwLhtT1DCK+DGSJkCEAgDwQQXjuyHJfw6ukM3//eLVlZCjfPhoYGeduj60X/n5d9BBBe+3JSMaLf/L9+V779w2sV29EAAhCAAATiI/DY+m75xPb/URoTFJzW1mZpbW2R27fvxzcxx3qOS3gXF5fkiy9+Wa5fvRWKyFu63iQf2/+z0t6xJlT7ahrdv39fhoeHpbe3N9Gno6U1bjVswrZFeMOSsqjdX377iswtzlsUEaEkRaBBGqSlpVHmF0JuOSQVGOMkRqCluUn0QPzlag4HTSy6fA3U3NAk79zwWKKTRngfxB2n8E49///Jd771g1A5fmTLQ/JLv/xETcJrxPL8+fOFsfQRwJOTk9LT0yNpiWda44YCXmUjhLdKYDY0v3dvXu7enbMhFGJImIAear5hQ4fcvHkn4ZEZzhYC69e3e5//Bb702JKSRONAeLMtvP4d3HPnzsmlS5dkZGRE2tp4elu9HzSEt16CKVyP8KYA3ZIhEV5LEpFiGAhvivAtGBrhzY/wXr58WcbHx2ViYsITXn9Jg5HhdevWyYsvvigHDx4sgBkcHPT+2787+/TTT3ttpqenvff27NlTEOmrV6/K0NCQfOQjH5Hjx49772tb7YcdXgs+9HkOAeHNb/YR3vzm3swc4c33GkB48yO8KrXXrl0rKp763tGjR+X06dOyc+dOD4qK64kTJzxJ7uzsFL8w63v60ra3bt3yhLavr8+rB9b3Dhw4ILt37/bGMu8fPnxYHn/88VRqh+P4lLPDGwfVmPtEeGMGbHH3CK/FyUkoNIQ3IdCWDoPwZlt4/TW85XZaS5U7nDx5Urq7uz2R9f93kJq+py8VXLPDOzY2tqpeuL+/H+G19O+B3ISF8OYm1Q9MFOHNb+7Z4SX3SgDhzbbw+mt4/eUEuvtarKQhWN+ru7pTU1Py6U9/Wj7zmc/IkSNHPInVl0qulkeYlylbQHj5u8VaAgivtamJPTCEN3bE1g/ADq/1KYo1QIQ3P8KrMzVlDYcOHQolvEaSf/jDH8q73/1ubwfXyO7s7Gyhbpcd3lg/pnQeFYEbf/mKLP4Dx5JFxdOpfhpEWlqaZGGeY8mcyluEwTa3NMry0oosL69E2Gu+u2pobpa2n3qXExAQ3vwIby07vEaSg/W9fsE1/XZ1dVHS4MSnPsdBfnn4N+QfXn01xwSYOgQgAIHoCKx9rEe2fOpfSkNTU3SdxtQTwptt4Q3W8I6Ojnr1uMHTEsodWWbKGvzlDubGtJmZGdHzfTdu3ChPPPEEwhvT55RuIyLwnwePyv3/8q2IeqMbCEAAAvkm0LbtnfK2Tw8hvI4ugyw8eCJK9Lqb+9RTTxVOb4iyb5f74pQGB7OH8DqYNEKGAASsJYDwWpuaUIHFJbz6RMMvX35N/uHeYqg41rQ2yfYPbJG1bdE/WjhUAEWOJgt7XR7aIbwOZhnhdTBphAwBCFhLAOG1NjWhAotLeHXwlZUVqeYp3npjMS87CSC8dualbFQIr4NJI2QIQMBaAgivtakJFVicwhsqABo5QQDhdSJNq4NEeB1MGiFDAALWEkB4rU1NqMAQ3lCYct8I4XVwCSC8DiaNkCEAAWsJILzWpiZUYAhvKEy5b4TwOrgEEF4Hk0bIEICAtQQQXmtTEyowhDcUptw3QnhjWALBx/RFPQTCGzVR+oMABPJMAOF1O/sIr9v5Syp6hLdG0rdu3RJ9DvX09HShB3NANMJbI1QugwAEIJACAYQ3BegRDhmX8OrpDLPfuy4ry8vhom1olM1bt0pDAyc1hAOWbCuEt0beRngPHz7sHe5sfu7r6/OeihLnix3eOOnSNwQgkDcCCK/bGY9LeBcXl+T/PPtvZOY73wgFqPOt/0R+8Z/9T9Le0R6qfdSN9AlrAwMDXrenT5+Wzs5OOXDggOjT1cyGXNRjutQfwltjtoLCq93oo/6uXbsmzz77rAwNDcnY2Ji34II7wfpYv8997nPy/PPPS/AxgrpIVaD1SSkTExNedNp+cnJSenp6vJ8R3hqTxmUQgAAEihBAeN1eFnEK74UXfl++/+o//ia3HKmNm7vlIx8frEt4/Y8ANmMZLyg3dtBJgo8idjvD0USP8NbIsZjwqqTqyy+8RlLNMKbN4ODgqpH9z8XWhXrhwgXZv3+/10avmZ2dFfNcbIS3xqRxGQQgAAGEN3NrICvCa3Zo/YKrrqEbXocOHZK2traSuQuWUhZzlMwlvsoJIbxVAjPNg4vJfCtTOdVdXbPD6xdeldozZ854O7faxrx0kasA+3dx/WHp++Pj44XrEN4ak8ZlEIAABEoI77Zjx6RpTYv1fJqaGqW5uVHm5sI97tb6CUUQYHt7PI/y1ZKGpHZ4w+7IqkccPXrUo7Znzx5vI+zGjRuF0gX98/e85z1eHfHXv/51r53/t8TFrjciXe69CNKUehcIb40pKHbTmvlWVuymtVI3spX6FuavxdEQd+zYgfDWmCsugwAEIFCOgJY09KjwtjRbD0plt6mpSebmFqyPNakAOzpaYxkqSeENc7N7cPPL/9tflV7/Rlupskv/ppte393d7d13FNyQ878XC9wUOkV4a4Re7tcFYX+1UOobXXDHlx3eGpPEZRCAAARCEKCGNwQki5tkoaRBveHEiRPeb3P9vwH2Yw+WRPpdQ9uVE95ivqFuMTU1JceOHZPjx49Lb29v4aZ7854ppbQ4/aFDQ3hDo1rdsBrhDVO366/NCQpu8JsXJQ01Jo3LIAABCBQhgPC6vSyyIrzFSiGDwmt2ZPXP1UP0pKgjR454zcIIb/BGeS2LMMJb7D2E1+3PRiTRhxVeHcwcC2IG1nqa3/7t3/a+zfnP8dX3tSzi8ccfl+Hh4cIJDk8++aTcvn2bkoZIMkcnEIAABFYTQHjdXhFZEN4wNbxR7PD29/d7J0H5X2bsYu+5vTJWR88Or4PZZIfXwaQRMgQgYC0BhNfa1IQKLAvCqxOtdErDlStXVt3gXm8Nr4ru2bNnZd++fd7JUP76Xv975U6HCJUgSxohvJYkopowEN5qaNEWAhCAQHkCCK/bKyQrwqtZqHQOb6mTFMLeO+S/XsfzP5Ci3Htur5AfR4/wOphFhNfBpBEyBCBgLQGE19rUhAosS8IbasI0qokAwlsTtnQvQnjT5c/oEIBAtgggvG7nMy7hXVpalq//1SWZu38nFKCWNWvlXT+zS9auXRuqPY2SJYDwJss7ktEQ3kgw0gkEIAABjwDC6/ZCiEt4lcrKyoqsrITn09jYEL4xLRMlgPAmijuawRDeaDjSCwQgAAGE1/01EKfwuk+HGRgCCK+DawHhdTBphAwBCFhLgB1ea1MTKjCENxSm3DdCeB1cAgivg0kjZAhAwFoCCK+1qQkVGMIbClPuGyG8Di4BhNfBpBEyBCBgLQGE19rUhAoM4Q2FKfeNEF4Hl8BXhn9T7l991cHICRkCEICAfQTaHuuRzb/6v0hDU5N9wQUiam1tltbWFrl9+771sSYVIMKbFGm3x0F4Hczf9y+9Iktz8w5GTshREGhpaZKFhaUouqIPBwk0NzeJHpekd4/ziohAU7O0v+tdEXUWbzcI74N84xJe/Yi9MfMjkeVwOV2RBnnobeukoYGTGsIRS7YVwpss70hGu3dvXu7enYukLzpxi4AeebNhQ4fcvBnuXEi3Zke0YQisX9/uff750hOGVvbaILzJCe/i4pLM/u/fkvnvvxFqITVvbJNH9rxbWttbQrVPslHwSWz1jK2PQB4fH5eJiQm5deuWDA0NydjYmPT09NTTbezXIryxI45+AIQ3eqau9IjwupKp+OJEeONj60LPCG+ywnvji9+Q+es/CrU0mt/SLps/9nhNwnv//n0ZHh6W1157zRPJzs5Ob0wVyoMHD3r/7f/zUAH5GpUT3mplOHPCe/LkSZmdnZWRkREPmSbi/PnzsnnzZpmcnLTe5KtdDC61R3hdyla0sSK80fJ0sTeE18WsRRczwptt4f3hD38oH/7wh2Xv3r3eRM+dOycXL16U27dvWyO8/gxUK8vRfRKq76noDq/5RnH48GHZuXOnqM1PTU158nvlypXCf7e1tVU/IlfUTQDhrRuhsx0gvM6mLrLAEd7IUDrZEcKbbeF95pln5E/+5E/k2LFj3kSPHz8u+me60ejf4dVNSf1ZX7oDPDg4WACjzjYwMOD9bDYp9b/9pQcqqgcOHPCufeWVV7wNTfM6ffq0t6mp701PT3t/vGfPHs8B1fvK7fCafmdmZorGluaHrqTwquweOXLEm7SC1ZcC1cmcOHHCq98wW+5pTiCPYyO8ecz6j+eM8OY392bmCG++1wDCm23h7e/vl+vXr3uTfPTRR73/1v83NbPqXbrre+nSpVW/ge/q6vIcTWVU/9/8Jl6dTcslVFSN8Goffscrtkur/ehLNz3NJmhfX5+381xKeLds2eJVA+gc9Dod9+zZs7Jv3z5v/LRfRYXX1JJo0Mby/bu9fvBpTyCP4yO8ecw6wpvfrK+eOcKb75WA8GZfeNW71Lkeeughb6dXhdR4l4qjSmVvb2+h7MEvoCq6ZoPST8pIrUqv9mWcTtuEKUvwb3yWEl4Vad0VNmJs2ye15E1r/m1ps11uLH/Xrl2rts9tm1TW4/n6V2Zkfn4x69NkfsUINIh4x5LNcyxZXhdIc0ujLC2tyMryg8eSNTSKbNj4JmnvsO8u8bzmK+p5I7zZF17dHVXB7O7ufmBH1Qiv2UU1wmp+867Ca64LCq+WMGipgb88oZzw+ssmtJ1xwWpKGrQ8Qudjw4tTGmzIQpUx/MGpP5fZG7ervIrmEIBA1gmse3Ob/OJH3yNv3pD+rw+zzjqt+SG8+RBe/yz9glnPDq8Kr0qslklcu3atsHFZbIfXf3CBjhlmhzd4LJk/bhtKYBHetP7WqmPcf/M7fyYz3wt3TEodw3ApBCDgGIGH1rfJ7r7HEV7H8lZNuAhvvoXX1PCeOXPGu2nNCHCpGl5Ti6vX+W9a8wts8KACJex/35S5+scodg6vjnHhwgXZv3+/lySEt5pPNm2LEkB4WRgQgEAxAghv9tcFwovwGiEtdUqD3tR29OhRD1SpUxqCEuu/RssQVF5NCYT2sXHjRnniiScKN8YVE15z05o58cG2Y2xL7vAaGP6zd81k/MXS2f/rxb4ZIrz25YSIIGADAYTXhizEGwPCm03hjXfV0LsSKCm8pmB69+7d3jFkzz33nHdig/9MXhuOmchjGhHePGadOUOgMgGEtzIj11sgvMkJ79LSstz86qyszIW8SbilUTrf+1ZpWctNozZ+ziqew6u7un7h5Rze9NOI8KafAyKAgI0EEF4bsxJtTAhvcsKrI62srMjKgweilEyqnpXOy04CVQsvO7zpJxLhTT8HRAABGwkgvDZmJdqYEN5khTfa7NFbmgRKljSYJ3nooce/+7u/65U02H6ocJogkxwb4U2SNmNBwB0CCK87uao1UoQX4a117eT9urLHkvmfx2xA2XSIcNLJS+uIjeC4CG/SmWc8CLhBAOF1I0/1RInwIrz1rJ88X8s5vL7sFxN882QRbYbw5vmjwtwhYD8BhNf+HNUbIcKL8Na7hvJ6fVnh9Z/LpoBsO1Mt6qQFhbbYYcxRj1lLf+zw1kKNayCQfQIIb/ZzjPAivNlf5fHMsGwNr3mSh3kknJ7QYB5NZ8uzkaPEEhRecxaxOXfY/76Oq7u/+kzq559/3gtj37598u1vf1tGRka8p5/oy/+0kuAXCFMeYsTa9BV81jUlDVFmmb4gkF0CCG92c2tmhvAmJ7x6OsPMzN94JzWEezXIli1vlYYGTmoIxyvZViVPaVCZO3z4sATFNsunNBTb4VUGR44cKZxBbJ4uYoR369atBcFVcfW39z+fWo93O3v2rCfFKsMqv+YLRbAv/Xl4eFiKibZ++WCHN9kPCaNBwBUCCK8rmao9ToQ3OeFdXFyS//gfL8r3v/+9UAl7+OGN3iZYe/vaUO3TahTczAsTR9hrbP3NuM6x4rFk+rAJ/yvL5/AWq+H1l3EU2+ENfilQkb127Zr3+D3/fwcXlF+GzekX/r7Mgz/27t37QO0wwhvm40kbCOSPgArvR597v2x8y5vyN/mczLipqVGamxtlbm4xJzOuPM329jWVG9XQQoX33//7/yDXr3831NWbNr1FPvaxvTULr5HF6enpwngq0P7fGocKpEKjsPLq7ybsNc4Jr5lYf3//Azu8WRdes4Nryjj8kqtzD+7wBoXX8Pmt3/ot+exnPyt+hqYkREsW9GVkGuGN4iNMHxCAgArvs7/0PoQ3w0tBZbepqUnm5hYyPMvqptbR0VrdBSFbJym8pYTy85//vDzzzDPeb5mjeoWV11rGc054dZKlShfK7VrWAsema4qdwuAvU9D/riS8Oh/dnf3mN78p69evL3wzC9Y/s8NrU+aJBQLZIEBJQzbyWG4WlDQ8SGfTpnWxJD5J4fU7QSm5DW6a+U+RMs9OWLdunbz44oseD/8xssV2j0dHR2X37t1eCaXZnAtuahrnO3To0KpSS+0/2Kf29/TTT6+6vyl4T5Lxy4GBAS/GHTt2yMTEhPecB+NP+rO+oj4ooWwNr39bvdRqCgYby6pLqNNiwlvtDq8/mZp8LUnQV3Axa79a9jA5OVl4oAclDQklmmEgkFECCG9GE+ubFsKbTeE18ui/Lyg404sXL8q2bdu83d7gJpq5Kd5Irt9d9L4hldquri7PO4I7vP4SymA/5j0jxubeIhNvX1+f5zna50svvSS7du3yhNfMQ+cQvCfJuI/Ow7+JGtxo1Z/1FdUhCZzD61tRxWp4/UIfpobXyO2JEye83WDzrSX4zWX79u3eyGNjYwhv9v+NYoYQSIQAwpsI5lQHQXizKbw6q1K7sGbjzD/zoLSaHV5T7+vfqdV+h4aGPN9QySx2AtXU1JSYJ+u+4x3vkNdff110V1ddRp+0qzfel7uZ3sRWrKTBL9T+k6uCvvSlL32pcDO/352i+sAhvFGR9PWjC09fxRZpFMNx01oUFOkDAtkjgPBmL6fBGSG82RXe4MzMJpzZtTWiev78+UJT85vkSsLr34QLCq+R41//9V+XP/qjPxItN9B7kH7lV35FPve5z3kibHaJ/adHqSQHb6gLI7ymZMFMwr+xqEJs3o/6hj2EN+K/H4NHk0XcvdcdwhsHVfqEgPsEEF73c1hpBghvfoTXL6ampKBUWUIl4S23w6vjqBDrzm57e7u3WWd2YpV2sTKIUk+eDSO83d3dFTcE47ixrmwNr6nNqPQB5P1kCSC8yfJmNAi4QgDhdSVTtceJ8GZTeHWX9Qtf+IJ37r95cFXwLH9/SUGwhrac8AZ3Z039r5YsmN9Eq+BeuHDBu69Iyx7M7rLZQQ4KaHB8/VmvVzEPPscheMyqv4ZXs/nCCy9417388suFGuXEhFcDCNaz+u8GrP2jypVREEB4o6BIHxDIHgGEN3s5Dc4I4c2m8BYrVwieUuD3Mn1v48aN3ukKKq3lhFfrYf0nPDz11FMexA996EMF4Q3eMBb8bXUxAQ2eGuE/paHUTfjl/DJu7wxV0hAspEZ+0/1LFeFNlz+jQ8BWAgivrZmJLi6EN5vCG90KoadSBEIJb9Di/Z0hv8kvLoQ3eeaMCAEXCCC8LmSpvhgR3uSEd2lpWb761a/J3NxcqKS1tKyR9773XbJ2bTwPwggVBI1KEigpvOYsNnNlMbHVnd/jx497d/DFcYQEeStOAOFlZUAAAsUIILzZXxcIb3LCqyOtrKzIykr4ddXY2BC+MS0TJcBNa4nijmYwhDcajvQCgawRQHizltEH54PwJiu82V9R+ZlhSeHVgmO9WzD4iLtSjxzOD7L0Z4rwpp8DIoCAjQQQXhuzEm1MCC/CG+2Kyk9vVQtv8DnL+UFlz0z/4HN/IbPfv21PQEQCAQhYQeChN6+VD330PfLmDW1WxEMQ0RNAeBHe6FdVPnqsWniDR1/kA5Nds/zaV2Zkfn7RrqCIJhECDdIgLS1NMr9A/hMBbuEgLc1NojfTLBcpLGxoaJDOjW+S9jc1Wxg5IUVBAOFFeKNYR3nsY5XwljuNwcAJnguXR2hpz/nevXm5ezfcXaNpx8r40RLQGyI2bOiQmzfvRNsxvTlDYP36du/zv7Cw5EzMBBodAYQX4Y1uNeWrp6p3ePOFx87ZIrx25iWJqBDeJCjbPQbCa3d+4o4O4U1OePWXKD+6dUNWVsJ+uWyU9Q9vEf1NCy/7CIQ6h9e+sPMdEcKb3/wjvPnNvZk5wpvvNYDwJie8i4tL8t0rU3L3h1dDLbo17V3y2I7npHWtWzX05uFi/qejhZqwY40QXscSpuEivA4mLaKQEd6IQDrcDcLrcPIiCB3hTVZ4r375D+Tu3/91qMy1drxNtn3gYE3CW+zRwnE92OvkyZPefAYHB73/R3hDpZdGaRBAeNOgbseYCK8deUgzCoQ3Tfrpj43wZlt4e3t7Ze/evWIEuKurqyCmUa0+hDcqkvQTOwGEN3bE1g6A8FqbmsQCQ3gTQ23lQAhvPoRXZ+l/7sGFCxfk6NGjhcmfPn1adu7c6f2sAnvnzh1544035Pz58zI6Our9ebH22ufAwEChnz179siv/dqvyac+9SnR/37++edlZmbG+++RkRFpa/txeYaOMTEx4f23q4cXUNJg5V9p5YP61lenZWF+3sHICTkKAnosGXfoR0HSrT6a16yVh7u6BeF1K29RR4vw5kd4zTGwx44d80R23759noDqn585c8YT0M7OTk9GVYgnJye9h4Xp7vDZs2fLtleKwZKGrVu3epKrr+HhYTG7zVryoP3v37+/IL+zs7OrhDjqdR5HfwhvHFRj7vPf/etx+cGN78Q8Ct1DAAI2EXhs+z+VHb/wrGzYwLFkNuUl6VgQ3nwIr6mr7evr80oc/C89QnZoaEjGxsY8wQ2WKAQJVWpfrIZX++zu7n5gbO1bd4nHx8cLwp30Z6DW8RDeWsmleN2Zz43I330/3F2jKYbJ0BCAQIQEtr3vF+T9//XHEN4ImbrYFcKbbeHVnVzz0tIEI7vB5yT4ywqKCW817cMIb7AUYseOHe4Kr5nw9PR0xb8DXJxoxUk51ADhdShZhAqBiAggvBGBdLwbhDfbwmvKCII7ugcOHPB2crVut9KOrZHdsO0rCa/KrpY/mJKJzO3wFtvONncN9vf3F4qlHf+7w8nwEV4n00bQEKiLAMJbF77MXIzw5lN4/SUMQQEN7vAGhTjY3tQGm5vSwgivv4QhWEPsyoer6iet+e8aNHfvuTLZrMSJ8GYlk8wDAuEJILzhWWW5JcKbP+HVGftPSdi+fbsHoVwNb7n2/t/o+09p8D94wr/pGTwj+Mknn5Tbt2+7W9LgX0IKQyd+5MgRryA6uLV+4sQJr2BZ7w7klTwBhDd55owIgbQJILxpZ8CO8RHebAqvHasr21EU3eE1Nl+slsTV2o0spRHhzVI2mQsEwhFAeMNxynorhDc54V1aWpbZ66/IytK9UMuqobFV3vK298ma1rWh2tMoWQIlT2kI1nxoWOWOyUg27HyPhvDmO//MPp8EEN585j04a4Q3OeHVkVZWVmRlJfza04cD8bKTQNljyYqd3OB/uoedU7IjKn/Ni3liyalTp7yal3pPuUB47cgxUUAgSQIIb5K07R0L4U1WeO1dCURWLQHO4a1AzF/4rU2Dj9srdXnwLsgoS0EQ3mqXOe0h4D4BhNf9HEYxA4QX4Y1iHeWxj7LCGzxoWAHlZYfX7NDqnP3Pk1aRffTRRyseyxY8JiQowPUsNoS3HnpcCwE3CSC8buYt6qgRXoQ36jWVl/7K1vAGHx1nDjM+dOhQ0cfNZQlamB3ZYMmH+TIQ3BV+9tln5Ytf/GIBz8GDB71DnEtdrw3LvYfw6gHtcAAAIABJREFUZmmlMRcIhCOA8IbjlPVWCC/Cm/U1Htf8yp7SUOwBE3k5h7fSs6mDJ1kEn2xSaYc3eAOgXm+Oe9NkqxSbZ2j739Oj4BDeuD4O9AsBewkgvPbmJsnIEF6EN8n1lqWxqn7wRFC+sgTDP5diT5rzvx98kom+55fcSsIb3EH2P8VO+/LvrgefcIfwZnXVMS8IlCbwzvc/Jb0f/u+ko2ONzM8vyvJyFbeOAzYzBJqaGqW5uVHm5hYzM6d6J9LevqbeLoper6czXPvBG7Is4T5rej7D2zeuk4YGTmqIJSF1dsoObwmAlXZ4i4m/1uleu3bNK1cII7wDAwMPjK5lEfoq9Z4+RxvhrXPVczkEHCTwY+HtF/3HfWFhSfSMUF75I6Cy29TUJHNzC/mbfIkZd3S0xsJicXFJJr/yXfnW34c7h3dLR6v88s+8Q960tiWWeMp1WslZSl1b7rkLiU8i5gFL1vAWe1YyNbz/mI0odninpqZW3RBneq9UNoLwxvypoHsIWEiAkgYLk5JCSJQ0PAh906Z1sWRChffUX31bvn7rTqj+t75prfyLndtqFt5iR8GGPRkK4a2cIk5pKMGo0ikNjz/+uAwPD0tXV5e3o1tvDa+GoaKrL32cs7+G1/8eO7yVFzUtIJBFAghvFrNa/ZwQ3mwKb6md1s9//vPyzDPPeF7ADm/1nxf/FZzDW4FfuXN4y52kUKmkQYcNXu9/IEW599jhrW/RczUEXCSA8LqYtehjRnizKbzFfmscnKnZWJuZmfHeMic+6X8HncPvLps3b5bJycmCNBfbSR4dHfVO3wq+F3aHOfqVHn2PCG/0TGPvEeGNHTEDQMA6AgivdSlJJSCEN5vCa0Rz69atRUsdddYXL16Ubdu2eeJa7rfK2teFCxdk//79HiyV39nZWa9fffl/Ox3cWQ7esP/CCy/I7t27RU+Icv1FSYODGUR4HUwaIUOgTgIIb50AM3I5wptN4dVZldt5Dc66mKhqGy2xDL78p0LpGENDQzI2NuaJs78fFVu/DGfkI1OYBg+ecDCjCK+DSSNkCNRJAOGtE2BGLkd4syu8xURVT2wyD7Uycnr+/PlCU1OKECxpCD4p15RMqvCaM/911zYozkHpNv1n4ePDsWQOZhHhdTBphAyBOgkgvHUCzMjlCG9+hLfc7mu5HV6VXd3pNXW7YXd4tYbX/wpTV+zSx4oHT7iUrZ/EivA6mDRChkCdBBDeOgFm5HKEN5vCq3L5hS98QY4cOSJtbW3eJP3CuWXLFq/coLe3d9XNZeaJrP4d3uCDrfzHzGrf/n78x81qScPZs2dl3759Xgy5EN7gk738y6vSGbEZ+TvF6mkgvFanh+AgEAsBhDcWrM51ivBmU3iLlSsET1fwlynoexs3bpT+/n5PgP3CG+zrySeflNu3b8vExIR385n/tIennnrKA/qhD32o0I+2My9TTuHcB6VIwDx4wsEsIrwOJo2QIVAnAYS3ToAZuRzhzabwZmR5Wj0NTmmwOj3Fg0N4HUwaIUOgTgIIb50AM3I5wpuc8Orju//82utydzHcY7zXNjXKE//kYWlrTf7RwhlZ3rFOY5Xw6t15hw8f9mpIKj3VI9ao6LwsAYSXBQKB/BFAePOX82IzRniTE14daWVlRVZWwq+9xsaG8I1pmSiBssKLACeai9CD/bt/fVJuznwndHsaQgAC7hN4x+O9suMXnpUNG9rl7t05WVhYcn9SzKBqAghvssJbdYK4wFoCCK+1qSkd2F9PT8vC/LyDkRNyvQQaGkRaWppkfh7ZqZela9e3tLZK5yPdsmFDB8LrWvIijBfhRXgjXE656grhdTDd9+7Ne//g8cofAf11mQrPzZt38jd5ZuwRWL+eHd48LwWEF+HN8/qvZ+4Ibz30UroW4U0JvAXDIrwWJCHlEBDelBOQ8vAIL8Kb8hJ0dniE18HUIbwOJi2ikBHeiEA63A3C63DyIggd4UV4I1hGueziAeE9ePCgTE9Pl4VhnsmsBxjzSp4Awps8c1tGRHhtyUR6cSC86bG3YWSEF+G1YR26GEPZc3hdnFAeYkZ485Dl4nNEePObezNzhDffawDhRXjz/QmoffYIb+3sUrsS4U0NfeoDI7yppyD1ABDe1FOQagAIL8Kb6gJ0eHCE18Hkvf7Nv5UljqVyMHMRhNzQIC0tjbJA/iOAmWAXjQ3S/MibIhkQ4Y0Eo7OdILwIr7OLN+XAEd6UE1DL8NdenJbF1+/WcinXQAACKRBo2tQhHf/N26UhgqcwIbwpJNCiIRFehNei5ehUKAivU+n6cbDf/cJXZPFvOYfVwdQRck4JNL+lQzo+9BjCm9P8RzlthBfhjXI95akvhNfBbCO8DiaNkHNNAOHNdfojnTzCi/BGuqBy1BnC62CyEV4Hk0bIuSaA8OY6/ZFOHuFFeCNdUDnqDOF1MNkIr4NJI+RcE0B4c53+SCeP8CK8kS6oHHWG8DqYbITXwaQRcq4JILy5Tn+kk0d4Ed5IF1SOOkN4HUw2wutg0gg51wQQ3lynP9LJI7wIb6QLKkedIbwOJhvhdTBphJxrAghvrtMf6eQRXoQ30gWVo84Q3hqSffLkSe+qwcHBGq7+x0vu378vw8PD0tvbK3v37pWw/SK8dWHnYggkTgDhTRx5ZgdEeBHezC7umCeWSeG9fPmyDAwMyOjoqCeS5nXu3Dk5evToA39eLeNyYhpWWnVMhLda8rSHgJsEEF4382Zj1AgvwmvjunQhpswK79jYmGzYsEHGx8els7NTbt26JYcPH5a///u/l/7+/lUiXG2iohLe4LhhZZkd3mozRnsIpEsA4U2Xf5ZGR3gR3iyt5yTnklnhnZqakh07dkh7e7snt7q7e+/ePZmeni6UEChos+ur/71nzx4ZGRmRtrY2LwcqyQcPHvSu0ZfZMQ6Kqf788ssvyyc+8Qn55Cc/Wcif6e/ChQvezrJ5nT59Wnbu3Flxh1f7nZiY8C7bvHmzTE5OSk9PD09aS/ITwlgQiIAAwhsBRLrwCCC8CC8fhdoIZFp4P/3pT8tnP/tZ0f9XydQyB/3Z1Mxq6YPuAKtU6i6wCubs7KwnvVpuoLLb19fnCbP+/NJLL8mHP/zhVbW2KszXrl0r1PMGZVivO3v2rOzbt88TaW1/5swZb0z9uVQNr8am0m4EXH/Wl4oyO7y1LXaugkBaBBDetMhnb1yEF+HN3qpOZkaZFl6VRd1dvXjxoiequ3fvLnuT2NWrV2VoaEi0HEJ3d/0y7E+Hkdru7m65dOnSql3hSmUJ/jG2bNlSMh6/GKuM+18IbzIfDkaBQFQEVHg3/rfvlqaWxrq7XLu2RebnF2V5eaXuvujAPQJNTY3S3Nwoc3OL7gUfU8Tt7Wti6plus0Qg88J748YNOXHihCevxXZUVVrNjW2mzvfIkSOe8Pp3WIPCa0oNTHmCeb+Y8KrkHjhwQGZmZrxmpjyhnPBqO39Jg7/cAuHN0keQueSBgArvwx95V2TCu7CwJEtLy3lAxxwDBFR2m5qaZG5uATY/IdDR0QoLCFQkkHnhNfW4SqLSqQjV7PBq6YOWSnzmM58RFWStrTWSqv9vjiwzsqvyquUIYXd4/ZkLxo3wVlzXNICAVQQoabAqHU4HQ0nDg+nbtGmd0zkl+GQI5Fp4tS5WxdTcDFauhld3fLU8Yv/+/atqeP0Cq9KrpQj+Mofg+/4xy+3wahnGtm3bPJFGeJP5MDAKBOIigPDGRTZ//SK8CG/+Vn00M8618CrCcqc0BEsRSp3S4JdYrbc1JzuYMoRTp04VTlvYvn27lzmtEy4nvOYsYZNm7dPsGrPDG83ipxcIJEUA4U2KdPbHQXgR3uyv8nhmmEnhjQeVPb0ivPbkgkggEIYAwhuGEm3CEEB4Ed4w64Q2DxJAeB1cFQivg0kj5FwTQHhznf5IJ4/wIryRLqgcdYbwOphshNfBpBFyrgkgvLlOf6STR3gR3kgXVI46Q3gdTDbC62DSCDnXBBDeXKc/0skjvAhvpAsqR50hvA4mG+F1MGmEnGsCCG+u0x/p5BFehDfSBZWjzhBeB5ON8DqYNELONQGEN9fpj3TyCC/CG+mCylFnCK+DyUZ4HUwaIeeaAMKb6/RHOnmEF+GNdEHlqDOE18FkI7wOJo2Qc00A4c11+iOdPMKL8Ea6oHLUGcLrYLIRXgeTRsi5JoDw5jr9kU4e4UV4I11QOeoM4XUw2df+8Kuy+PpdByMnZAjkk0Dzpg5pf7pbGhob6gawfn273L07JwsLS3X3RQfuEUB4EV73Vq0dESO8duShqihe/8brssQ/dlUxy1LjlpYmZMe1hDY2SPMjHZFEjfBGgtHZThBehNfZxZty4AhvygmoZfh79+a9HR5e+SPQ2NggGzZ0yM2bd/I3eWbsEUB4870QEF6EN9+fgNpnj/DWzi61KxHe1NCnPjDCm3oKUg8A4U09BakGgPAivKkuQIcHR3gdTB7C62DSIgoZ4Y0IpMPdILwOJy+C0BFehDeCZZTLLhBeB9OO8DqYtIhCRngjAulwNwivw8mLIHSEF+GNYBnlsguE18G0I7wOJi2ikBHeiEA63A3C63DyIggd4UV4I1hGuewC4XUw7Qivg0mLKGSENyKQDneD8DqcvAhCR3gR3giWUS67QHgdTPs3v3lV5ucXHIyckOsl0NAgoseSzc9zBmu9LM31a9askY0bN4lI/WfkRhVTuX4Q3iQo2zsGwovw2rs67Y4M4bU7P0Wj+8M/PCOvv/43DkZOyBCwj8D73/9z8q53/bQ06LcJB14IrwNJijFEhBfhjXF5ZbprhNfB9P7bf/ui/O3fzjoYOSFDwD4CH/jALnnPe7YjvPalhoiKEEB4EV4+GLURQHhr45bqVQhvqvgZPGMEEN6MJTTj00F4Ed6ML/HYpofwxoY2vo4R3vjY0nP+CCC8+cu5yzNGeBFel9dvmrEjvGnSr3FshLdGcFwGgSIEEF6WhUsEEF6E16X1alOsCK9N2QgZC8IbEhTNIBCCAMIbAhJNrCGA8CK81ixGxwJBeB1LmIaL8DqYNEK2lgDCa21qCKwIAYQX4eWDURsBhLc2bqlehfCmip/BM0YA4c1YQjM+HYQX4c34Eo9tegiviJw7d04uXbokIyMj0tbWFgr2yZMnvXaDg4Ml29+6dUsOHjwohw8flp07d4bqN0wjhDcMJdpAIBwBhDccJ1rZQQDhRXjtWInuRWG18KpUTkxMFKhu3rxZJicnpaenJ1LSCG+kOOkMAk4RQHidSlfug0V4Ed7cfwhqBGCt8BaT0MuXL8v169dl7969NU63+GW1CG+YANjhDUOJNhBIlwDCmy5/Rq+OAMKL8Fa3YmhtCFgpvPfv35fh4WHp7e0tKbdXr16VAwcOyMzMjDcXLR0w5QVGYNetWycvvvii9/7p06dXlRUEd4/37NnjlTRcuHBBrl275vVl4ujv7/eu1TFPnDgh4+Pj3k6zvsyYRm6np6e9Px8dHZWnn37ai0v7fv75571YzTimdEIlfmBgwLtmx44d3o52Z2en97M/Rv/uNiUNfIAhEB0BhDc6lvQUPwGEF+GNf5VlcwQrhdfInspnqRKGixcvyrZt27zyBiO/Kogqpiq8R48eLUiuSqVKqpFJff/MmTOrfjY1vFeuXJGpqSlPfm/cuOFJ9e7duz2x1X7Me6dOnSoIr5Hdvr4+T9BVlF966SXZtWuXJ7xbt271+tOXX+S1P+3XzFHjMrLtH0vlWH/Wl84P4c3mh5FZpUMA4U2HO6PWRgDhRXhrWzlcZa3wBnc49efg7qhJX3BHOFii4N+ZVXkM7h7722tfepPZkSNH5Mtf/rL84Ac/kFdffVWOHTsmX/rSl7whVWr9N60FhdrEVaykQa/r7u5+oA+9xh+njuWXcv9SRXj54EIgOgI/93NPyM/8zAeksbExuk5j7Gnt2haZn1+U5eWVGEeha1sJNDU1SnNzo8zNLdoaYuJxtbevSXxMBnSPgNXC68dp5FF3Tf3lBufPny800zICldEwwmvKFPRif3v9WYVY33/llVfkmWeekS9+8Yvy1FNPyZ/+6Z96/6+7rEHhNTu//lMewgiv/6Y8Hdtf1uAvafDLPsLr3geNiO0l8LM/q8L7M04J78LCkiwtLdsLlchiI6Cy29TUJHNzC7GN4VrHHR2troVMvCkQcEZ4/WKqu63Hjx+Xrq6uVfJran7DCK+/PjjYXn/Wnd07d+7IoUOHxJQ5aAw6ttbYRrXDa3Z7y+U+uION8KbwSWHIzBKgpCGzqc3kxChpeDCtmzaty2SumVS0BKwUXhU8vTnsueeeKxxB5pc+ran1lyUEa2jLCa/KarEdXcVqzuE1N5KZHWPTv6nF1V1cv/AGx9eftf5Y4wyew+svaQjW8GoML7zwgnfdyy+/XKhRRnijXfT0BgE/AYSX9eASAYQX4XVpvdoUq5XCa3Zz9cYz/8sIqP6Z/3QDPcFg48aNXhlCpZIGFV4jkFoOodd+8IMflDfeeKMgvCqspo7XnPnrF1UdP/jgieCpEf5TGvwPngj245+H9mtOmyj159qGHV6bPkLE4joBhNf1DOYrfoQX4c3Xio9uttYKb3RTzF5PCG/2csqM0iOA8KbHnpGrJ4DwIrzVrxquUAIIr4PrAOF1MGmEbC0BhNfa1BBYEQIIL8LLB6M2AghvbdxSvQrhTRU/g2eMAMKbsYRmfDoIL8Kb8SUe2/QQ3tjQxtcxwhsfW3rOHwGEN385d3nGCC/C6/L6TTN2hDdN+jWOjfDWCI7LIFCEAMLLsnCJAMKL8Lq0Xm2KFeG1KRshY0F4Q4KiGQRCEEB4Q0CiiTUEEF6E15rF6FggCK9jCdNwEV4Hk0bI1hJAeK1NDYEVIYDwIrx8MGojgPDWxi3VqxDeVPEzeMYIILwZS2jGp4PwIrwZX+KxTQ/hjQ1tfB0jvPGxpef8EUB485dzl2eM8CK8Lq/fNGNHeNOkX+PYU1Pn5PXX/6bGq7kMAhDwE3jf+z4g73rXT0tDQ4MTYNavb5e7d+dkYWHJiXgJMloCCC/CG+2Kyk9vCK+Duf7GN67KwsKCg5ETcv0EGqSlpRHZqR9koYc1a9bIww+/RRzxXUF4I0y+g10hvAivg8vWipARXivSUF0Q9+7Nezs8vPJHoLGxQTZs6JCbN+/kb/LM2COA8OZ7ISC8CG++PwG1zx7hrZ1dalcivKmhT31ghDf1FKQeAMKbegpSDQDhRXhTXYAOD47wOpg8hNfBpEUUMsIbEUiHu0F4HU5eBKEjvAhvBMsol10gvA6mHeF1MGkRhYzwRgTS4W4QXoeTF0HoCC/CG8EyymUXCK+DaUd4HUxaRCEjvBGBdLgbhNfh5EUQOsKL8EawjHLZBcLrYNoRXgeTFlHICG9EIB3uBuF1OHkRhI7wIrwRLKNcdoHwOpj22e99QxYX5h2MnJDrJaBnxeqxZPPznMFqWDat6ZSW1g31onXmeoTXmVTFEijCi/DGsrBy0CnC62CSv/6X/0rm7nzPwcgJGQLREmhobJa3PNYva9rfFm3HFveG8FqcnARCQ3gR3gSWWSaHQHgdTOvX/uKUzN257mDkhAyBaAl4wtuzX1o7EN5oydKbrQQQXoTX1rVpe1wIr+0ZKhIfwutg0gg5FgIIbyxY6dRiAggvwmvx8rQ6NITX6vQUDw7hdTBphBwLAYQ3Fqx0ajEBhBfhtXh5Wh0awmt1ehBeB9NDyAkSQHgThM1QVhBAeBFeKxaig0EgvA4mjR1eB5NGyLEQQHhjwUqnFhNAeBFei5en1aEhvFanhx1eB9NDyAkSQHgThM1QVhBAeBFeKxaig0EgvA4mjR1eB5NGyLEQQHhjwUqnFhNAeBFei5en1aEhvCml5+TJk97Ig4ODVUeA8FaNjAsySgDhzWhimVZJAggvwsvHozYCuRbec+fOydGjRwvkduzYIRMTE9LZ2VkbzRJX6TiXLl2SkZERaWtr81ohvJEiprOcEkB4c5r4HE8b4UV4c7z865p67oXXL6IqpmfOnIlcehHeutYoF0OgJAGEl8WRNwIIL8KbtzUf1XwRXt/O661bt+Tw4cNy5MgRj++BAwdkZmbG+++DBw8Wyg90d/bOnTvyxhtvyPnz52V0dFTe//73F21/9erVVX9udpEnJydX9bF582bRP+vp6SnsAOtus76C71HSENXypx/XCSC8rmeQ+KslgPAivNWuGdr/mADC6xNeldOhoSEZGxuTV199VbZt2+YJqJFWFd2dO3d65QgXLlxYJagXL14s2b7UDq+/D3+b+/fve/3v37+/IL+zs7OFkgiEl48vBH7yFxiPFmYp5IwAwovw5mzJRzZdhNcnvCqyfrE0lFVAh4eHpbe3V/bu3Vux/jbYPkxJw+XLl2VqampVna8ZX98bHx8vlFogvJGtfzpynIDu8G7+qf9BHnr47Y7PJHz4a9e2yPz8oiwvr4S/iJaZIdDU1CjNzY0yN7eYmTnVO5H29jX1dsH1OSCQe+H137S2Z8+egnAaadWSBfPS0oVSwluufS3Cq5I7MDBQGNt/Qx3Cm4NPJlMMReDHwvvfy7rOfAnvwsKSLC0th2JEo2wRUNltamqSubmFbE2sjtl0dLTWcTWX5oVA7oU3eHqCJt7Ia1dXl1e3W2mHt1L7aoX3ypUr3rimppcd3rx8HJlntQSo4a2WGO1dJ0BJw4MZ3LRpnetpJf4ECCC8gePC/MJrShj0Zja9aa2vr6/oDm9QiIPtg8KqYwSPJfOXNKjw+ksYgqdHsMObwCeDIZwggPA6kSaCjJAAwovwRricctUVwltEeHUF+EsK9JSEjRs3Sn9/f8mShnLt/eUO/lMadBzz4Am/8Oqfa82wKad48skn5fbt29Tw5uqjyWTDEEB4w1CiTZYIILwIb5bWc5JzybXwJgk6yrHY4Y2SJn25TADhdTl7xF4LAYQX4a1l3XBNzo8lc3UBILyuZo64oyaA8EZNlP5sJ4DwIry2r1Fb42OH19bMlIkL4XUwaYQcCwGENxasdGoxAYQX4bV4eVodGsJrdXqKB4fwOpg0Qo6FAMIbC1Y6tZgAwovwWrw8rQ4N4bU6PQivg+kh5AQJILwJwmYoKwggvAivFQvRwSAQXgeTxg6vg0kj5FgIILyxYKVTiwkgvAivxcvT6tAQXqvTww6vg+kh5AQJILwJwmYoKwggvAivFQvRwSAQXgeTxg6vg0kj5FgIILyxYKVTiwkgvAivxcvT6tAQXqvTww6vg+kh5AQJILwJwmYoKwggvAivFQvRwSAQXgeT9vW/nJD5u685GDkhQyBaAiq8m97RL2vat0TbscW9rV/fLnfvzsnCwpLFURJaXAQQXoQ3rrWV9X4RXgczPPvaN2Rxcd7ByAm5fgIN0tLSJAsLi/V3lZEemtesl+bWhzMym8rTQHgrM8pyC4QX4c3y+o5zbghvnHRj6vvevXlvh4dX/gg0NjbIhg0dcvPmnfxNnhl7BBDefC8EhBfhzfcnoPbZI7y1s0vtSoQ3NfSpD4zwpp6C1ANAeFNPQaoBILwIb6oL0OHBEV4Hk4fwOpi0iEJGeCMC6XA3CK/DyYsgdIQX4Y1gGeWyC4TXwbQjvA4mLaKQEd6IQDrcDcLrcPIiCB3hRXgjWEa57ALhdTDtCK+DSYsoZIQ3IpAOd4PwOpy8CEJHeBHeCJZRLrtAeB1MO8LrYNIiChnhjQikw90gvA4nL4LQEV6EN4JllMsuEF4H0/7K9R/I3CJncDqYurpDbmgQ71iy+Xm386/zeHjNGtnQ2lw3k7x1gPDmLeOr54vwIrz5/gTUPnuEt3Z2qV158s//Wq7duZ/a+AwMgXoJdDQ3yS/1bJau9tZ6u8rd9Qhv7lK+asIIL8Kb709A7bNHeGtnl9qVo3/2TfnuGwhvaglg4LoJdLQ0ycffuQXhrYEkwlsDtAxdgvAivBlazolOBeFNFHc0gyG80XCkl/QIILy1s0d4a2eXhSsRXoQ3C+s4jTkgvGlQr3NMhLdOgFyeOgGEt/YUILy1s8vClQgvwpuFdZzGHBDeNKjXOSbCWydALk+dAMJbewoQ3trZZeFKhBfhzcI6TmMOCG8a1OscE+GtEyCXp04A4a09BQhv7eyycCXCi/BmYR2nMQeENw3qdY6J8NYJkMtTJ4Dw1p4ChLd2dlm4EuFFeLOwjtOYA8KbBvU6x0R46wTI5akTQHhrTwHCWzu7LFyJ8CK8WVjHacwB4Y2Y+uXLl2V8fFwmJiaks7Ozqt7PnTsnly5dkpGREWlrayt5LcJbFVYaW0gA4a09KQhv7eyycCXCi/BmYR2nMQeEtwT1kydPetJqXps3b5bJyUnp6ekpmyeEN41lzJiuEUB4a88Ywls7uyxcifAivFlYx2nMAeEtQr3YTquK7PXr12Xv3r0IbxorlTEzRQDhrT2dCG/t7LJwJcKL8GZhHacxB4Q3QP3+/fsyPDwsvb29JeX21q1bcvDgQZmenvau3rNnT6EMIbjDq/J89OjRwiinT5+WnTt3Fn4O7iT7+/K/599hpqQhjY8KY0ZJAOGtnSbCWzu7LFyJ8CK8WVjHacwB4S1CXUXzwoULJUsYVGr1peJq5Levr88TZL/wah3u2bNnZd++fV5NrsrvmTNnCvW9xX42NbxXrlyRqampVSJtxkR40/ioMGaUBBDe2mkivLWzy8KVCC/Cm4V1nMYcEN4S1MvtvAYv0bb6GhwcXCW8wZvWrl69KkNDQzI2NiZbtmx5YCfZX0qhwu2XY/+YCG8aHxXGjJKACu/B926V7vUdUXabi77Wrm2R+flFWV5eycV8meRqAk1NjdLc3Chzc4ug+QmB9vY1sIBARQIIb0VEUtjF3bVrlye1+goKsZY4FBNeldwDBw7IzMyMd50pTTDC29/fXyhxCNYO+8fwlzogvCGSRhOrCajw/vP3qPC2Wx2njcGp8C4sLMkHnY2tAAANMElEQVTS0rKN4RFTzARUdpuammRubiHmkdzpvqOj1Z1giTQ1AghvSPR+GT116pTMzs4Wyg1K7fBquYPKrr6v5Q/V7PD6jyUL1hUjvCGTRjNrCVDSUHtqKGmonV0WrqSk4cEsbtq0LgupZQ4xE0B4A4BVLk+cOCHPPfdc4QiyoHD6Bde819XV9cAOrwqvKWHQ48y0vld3gc3xZn6J1jD0Zjl96Tm8L730kmzbts2LAeGN+VNA94kTQHhrR47w1s4uC1civAhvFtZxGnNAeItQD56soE1GR0cLpzb4yxS0RGHjxo3yxBNPFC1p8JclbN++3RtNa3j9Inv+/Hmv1OGDH/ygvPHGG57w6k1rAwMDhehMyYQXy599U777xv001gtjQiASAghv7RgR3trZZeFKhBfhzcI6TmMOCG8a1OscE+GtEyCXp04A4a09BQhv7eyycCXCi/BmYR2nMQeENw3qdY6J8NYJkMtTJ4Dw1p4ChLd2dlm4EuFFeLOwjtOYA8KbBvU6x0R46wTI5akTQHhrTwHCWzu7LFyJ8CK8WVjHacwB4U2Dep1jIrx1AuTy1AkgvLWnAOGtnV0WrkR4Ed4srOM05oDwpkG9zjER3joBcnnqBBDe2lOA8NbOLgtXIrwIbxbWcRpzQHjToF7nmAhvnQC5PHUCCG/tKUB4a2eXhSsRXoQ3C+s4jTkgvGlQr3NMhLdOgFyeOgGEt/YUILy1s8vClQgvwpuFdZzGHBDeNKjXOSbCWydALk+dAMJbewoQ3trZZeFKhBfhzcI6TmMOCG8a1OscE+GtEyCXp04A4a09BQhv7eyycCXCi/BmYR2nMQeENw3qdY558s+/Jdfv/EOdvXA5BNIj0N7SKM/1dMkjba3pBeHoyAivo4mLKGyEF+GNaCnlrhuE18GU/9X1H8j84rKDkRNyFARaWppkYWEpiq5S7WNTa4usb21ONQYXB0d4XcxadDEjvAhvdKspXz0hvA7m+969ebl7d87ByAm5XgKNjQ2yYUOH3Lx5p96uuN5RAgivo4mLKGyEF+GNaCnlrhuE18GUI7wOJi2ikBHeiEA63A3C63DyIggd4UV4I1hGuewC4XUw7Qivg0mLKGSENyKQDneD8DqcvAhCR3gR3giWUS67QHhzmXYmDQEIQAACEIAABPJDAOHNT66ZKQQgAAEIQAACEMglAYQ3l2ln0hCAAAQgAAEIQCA/BBDe/OSamUIAAhCAAAQgAIFcEkB4HUr7uXPn5OjRo17Ee/bskZGREWlra3NoBoRajMDVq1flxIkTMj4+Lp2dnYUmt27dkoMHD8r09LT3Z6dPn5adO3cW3r98+bIMDAx4P+/YsUMmJiYK19+/f1+Gh4fl/Pnz3vujo6Oyd+9eEmARAc37gQMHZGZmpmgO9Q/LfebrWR8WYch1KP78FvuclvuM17s+cg2eyeeSAMLrSNr1Lz4VIiM1J0+e9CIfHBx0ZAaEGSTgF5ZSwtrb2+uJqsrR0NCQjI2NSU9PzwM/6z+cly5dKnwJ8q8PM87hw4dXCTMZSZeAfqavX79e+CKiOZudnS3ksNxn3nyhqXV9pDtzRlcCmsNTp055X3r0i27wcxr8zAc/4/WsDzIAgTwSQHgdybr+Y9jd3V34xzH4l50j0yDMIgSK7fAG/ywoOPqP37Vr1wpfePz/OOo/niq3R44c8eRYX3xBsn/pFROYUp95lSP/bwWqWR9mTdhPJF8RVpvDcv8mVFof+SLLbCHwYwIIrwMrIfgXoYYc/PbvwDQIsQSBYsJb7AuNX1qDAuvfHVLh9e8G67DB3SGSYR8Bf440Oi1JMTu4wc+85tv/G5/gl5py68NfFmMfhfxGZEpcNHeao3I5fPzxx+taH/mlzMzzTADhdSD7Rnj7+/sLv5JGeB1IXMgQSwnv1NTUqjrtoPD6d/+CwhusCUZ4QyYjpWbBz3Olz7zmu9b1gfCmlOQSw/pLm/y19sEdXP9n3AhvqX8TKq0PuwgQDQSSIYDwJsO5rlHY4a0Ln/UXs8NrfYpiDTC4s6eDVfrMs8Mba0pS6TyYc3Z4U0kDg2aYAMLrSHKp4XUkUTWESQ1vDdAyckkx2TVTq6dGs1yNNzW89i4ef94q5bCe9WEvASKDQHwEEN742EbaM6c0RIrTqs6KCW+9d+FzSoNVKS4aTKWypHruwq90h7/9dLIfoe7ST05OyqFDh7zjJU3JQl9fX9GTWTilIftrghnGSwDhjZdvpL1zDm+kOFPvLHiOqgak5+6ao+bqOWeVc3hTT2/FAIJnsJoL/Octcw5vRYxON9AvpnrUpHkFz8vmHF6n00vwlhFAeC1LCOFAAAIQgAAEIAABCERLAOGNlie9QQACEIAABCAAAQhYRgDhtSwhhAMBCEAAAhCAAAQgEC0BhDdanvQGAQhAAAIQgAAEIGAZAYTXsoQQDgQgAAEIQAACEIBAtAQQ3mh50hsEIAABCEAAAhCAgGUEEF7LEkI4EIAABCAAAQhAAALREkB4o+VJbxCAAAQgAAEIQAAClhFAeC1LCOFAAAIQgAAEIAABCERLAOGNlie9QQACEIAABCAAAQhYRgDhtSwhhAMBCEAAAhCAAAQgEC0BhDdanvQGAQhAAAIQgAAEIGAZAYTXsoQQDgQgAAEIQAACEIBAtAQQ3mh50hsEIAABCEAAAhCAgGUEEF7LEkI4EIAABCAAAQhAAALREkB4o+VJbxCAAAQgAAEIQAAClhFAeC1LCOFAAAIQgAAEIAABCERLAOGNlie9QQACEIAABCAAAQhYRgDhtSwhhAMBCMRL4P79+zI8PCznz59fNdDo6Kjs3bs33sF9vd+6dUsOHjwohw8flp07dyY2LgNBAAIQyCMBhDePWWfOEMgpgatXr8qBAwdk9+7dMjg4WKCg8qnieeTIEenp6UmEDsKbCGYGgQAEIOARQHhZCBCAQC4ImJ3drq6uVbJbbPJGjGdmZry3dSfWCLLpp7e3d9WO8MmTJ7222s7f5tq1azIxMeG9t2fPHhkZGfH+O7jLvGPHDq9dZ2dnLvLBJCEAAQgkSQDhTZI2Y0EAAqkRMBKrYlquhCDYLijK1Qivlk2cPn3aG8/s6Pb19XmizA5vakuBgSEAgRwSQHhzmHSmDIE8Erh8+bK3+zo5OVm2bOHcuXNy6dIlbye2ra3NQ6XXjo+Pezuw+me6Oxt2h9dfF+zfBUZ487gKmTMEIJAWAYQ3LfKMCwEIJEogrPCqlHZ3d68qV/DX+G7ZsgXhTTRzDAYBCECgfgIIb/0M6QECEHCAQNiSBoTXgWQSIgQgAIEqCSC8VQKjOQQg4CaBSjetXbx4UbZt2yZf/vKXEylpMPH09/dzLJmbS4qoIQABhwggvA4li1AhAIH6CJQ6lkx3dS9cuODV9+pLjy4zN7cVE2V9b3Z2tlDnq+USAwMDhdMcqrmxLcypEfXNmqshAAEIQADhZQ1AAAK5IlDswRPBI8HKHUumsIJ96LFl5hU8lqzUTWva3j8Ox5LlahkyWQhAIGECCG/CwBkOAhCAAAQgAAEIQCBZAghvsrwZDQIQgAAEIAABCEAgYQIIb8LAGQ4CEIAABCAAAQhAIFkCCG+yvBkNAhCAAAQgAAEIQCBhAghvwsAZDgIQgAAEIAABCEAgWQIIb7K8GQ0CEIAABCAAAQhAIGECCG/CwBkOAhCAAAQgAAEIQCBZAghvsrwZDQIQgAAEIAABCEAgYQIIb8LAGQ4CEIAABCAAAQhAIFkCCG+yvBkNAhCAAAQgAAEIQCBhAghvwsAZDgIQgAAEIAABCEAgWQIIb7K8GQ0CEIAABCAAAQhAIGECCG/CwBkOAhCAAAQgAAEIQCBZAghvsrwZDQIQgAAEIAABCEAgYQIIb8LAGQ4CEIAABCAAAQhAIFkCCG+yvBkNAhCAAAQgAAEIQCBhAghvwsAZDgIQgAAEIAABCEAgWQIIb7K8GQ0CEIAABCAAAQhAIGECCG/CwBkOAhCAAAQgAAEIQCBZAghvsrwZDQIQgAAEIAABCEAgYQIIb8LAGQ4CEIAABCAAAQhAIFkCCG+yvBkNAhCAAAQgAAEIQCBhAghvwsAZDgIQgAAEIAABCEAgWQIIb7K8GQ0CEIAABCAAAQhAIGECCG/CwBkOAhCAAAQgAAEIQCBZAghvsrwZDQIQgAAEIAABCEAgYQIIb8LAGQ4CEIAABCAAAQhAIFkCCG+yvBkNAhCAAAQgAAEIQCBhAghvwsAZDgIQgAAEIAABCEAgWQIIb7K8GQ0CEIAABCAAAQhAIGECCG/CwBkOAhCAAAQgAAEIQCBZAghvsrwZDQIQgAAEIAABCEAgYQIIb8LAGQ4CEIAABCAAAQhAIFkCCG+yvBkNAhCAAAQgAAEIQCBhAghvwsAZDgIQgAAEIAABCEAgWQIIb7K8GQ0CEIAABCAAAQhAIGECCG/CwBkOAhCAAAQgAAEIQCBZAghvsrwZDQIQgAAEIAABCEAgYQIIb8LAGQ4CEIAABCAAAQhAIFkC/z8X1cLG79hn+QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fav_10 = favorite_food[:10]\n",
    "fig = px.bar(fav_10,\n",
    "             x= fav_10,\n",
    "             y=fav_10.index,\n",
    "             color= fav_10.index,\n",
    "             template = 'seaborn',\n",
    "             height = 500,\n",
    "            width = 700\n",
    "            )\n",
    "\n",
    "fig.update_layout(\n",
    "    title = 'Top 10 Favorite Food',\n",
    "    xaxis_title = 'Count',\n",
    "    yaxis_title = 'Food types'\n",
    ")\n",
    "   \n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4927a0db-8dc9-4742-86ba-46b69e5ff879",
   "metadata": {
    "id": "4927a0db-8dc9-4742-86ba-46b69e5ff879"
   },
   "source": [
    "**Insight:** Five most liked dishes are Pasta, Pizza, Cocktails, Burger, Mocktails."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133f12a1-83ea-454c-93a3-8ca9308f1c62",
   "metadata": {
    "id": "133f12a1-83ea-454c-93a3-8ca9308f1c62"
   },
   "source": [
    "#### Most Popular Restaurant Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b171b446-8a60-4f8a-a707-5ea84b429837",
   "metadata": {
    "id": "b171b446-8a60-4f8a-a707-5ea84b429837",
    "outputId": "9e34d08d-d0f7-4f37-ebb6-d8aab756178b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rest = df['rest_type'].value_counts()[:10]\n",
    "fig = px.bar(rest,\n",
    "             x='rest_type',\n",
    "             y=rest.index,\n",
    "             color= rest.index,\n",
    "             template = 'ggplot2',\n",
    "             height = 500,\n",
    "            width = 700\n",
    "            )\n",
    "\n",
    "fig.update_layout(\n",
    "    title = 'Types of Restaurant',\n",
    "    xaxis_title = 'Count',\n",
    "    yaxis_title = 'restaurant types'\n",
    ")\n",
    "   \n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432e6e9e-ae4e-4cb6-8aa7-e20c6b74ce62",
   "metadata": {
    "id": "432e6e9e-ae4e-4cb6-8aa7-e20c6b74ce62"
   },
   "source": [
    "**Insight:** The 3 most common type of restaurants in Bangalore are Casual Dining, Quick Bites and Cafe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2762aa-b960-43f7-bf52-798a6c89f252",
   "metadata": {
    "id": "df2762aa-b960-43f7-bf52-798a6c89f252",
    "tags": []
   },
   "source": [
    "<a id='model'></a>\n",
    "## Model Building\n",
    "In this section we would so some more preprocessing and build a regresion model to predict the rating of a restaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6fdb1064-747c-41a5-bf08-317905f09b8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "import pickle\n",
    "\n",
    "rs = 123"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43340d8-fa5f-4273-a2e8-8ec1cfcc04c2",
   "metadata": {},
   "source": [
    "##### Read Cleaned Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "45c12c06-c782-43ea-bdbd-b2ce23bff87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>dish_liked</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>cost_for_2</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jalsa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Pasta, Lunch Buffet, Masala Papad, Paneer Laja...</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spice Elephant</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Momos, Lunch Buffet, Chocolate Nirvana, Thai G...</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Churro Cafe</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Churros, Cannelloni, Minestrone Soup, Hot Choc...</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name online_order book_table  rate  votes      location  \\\n",
       "0            Jalsa          Yes        Yes   4.1    775  Banashankari   \n",
       "1   Spice Elephant          Yes         No   4.1    787  Banashankari   \n",
       "2  San Churro Cafe          Yes         No   3.8    918  Banashankari   \n",
       "\n",
       "             rest_type                                         dish_liked  \\\n",
       "0        Casual Dining  Pasta, Lunch Buffet, Masala Papad, Paneer Laja...   \n",
       "1        Casual Dining  Momos, Lunch Buffet, Chocolate Nirvana, Thai G...   \n",
       "2  Cafe, Casual Dining  Churros, Cannelloni, Minestrone Soup, Hot Choc...   \n",
       "\n",
       "                         cuisines  cost_for_2    type  \n",
       "0  North Indian, Mughlai, Chinese       800.0  Buffet  \n",
       "1     Chinese, North Indian, Thai       800.0  Buffet  \n",
       "2          Cafe, Mexican, Italian       800.0  Buffet  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('zomato_clean.csv')\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1cf8dc69-25aa-4204-a7a3-97801cc38ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name            3157\n",
       "online_order       2\n",
       "book_table         2\n",
       "rate              31\n",
       "votes           2303\n",
       "location          88\n",
       "rest_type         73\n",
       "dish_liked      5194\n",
       "cuisines        1681\n",
       "cost_for_2        57\n",
       "type               7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c0bb8d-64c9-45a3-be4f-c8845a0ea79f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a1f676a-7586-4233-9a23-635bec94f8a3",
   "metadata": {},
   "source": [
    "##### Drop unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5a8678e0-ef7f-4a4f-bdd7-e16e0dda7d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>cost_for_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  online_order book_table  rate  votes      location            rest_type  \\\n",
       "0          Yes        Yes   4.1    775  Banashankari        Casual Dining   \n",
       "1          Yes         No   4.1    787  Banashankari        Casual Dining   \n",
       "2          Yes         No   3.8    918  Banashankari  Cafe, Casual Dining   \n",
       "\n",
       "                         cuisines  cost_for_2  \n",
       "0  North Indian, Mughlai, Chinese       800.0  \n",
       "1     Chinese, North Indian, Thai       800.0  \n",
       "2          Cafe, Mexican, Italian       800.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unwanted_columns = ['name', 'type', 'dish_liked']\n",
    "\n",
    "data = data.drop(unwanted_columns, axis=1)\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e7553b-4746-4b42-8cc9-1aab1c621288",
   "metadata": {},
   "source": [
    "##### Convert the online categoriccal variables into a numeric format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0ad98764-7ff7-4c83-8c88-a57ceb9cddea",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "data['online_order'] = label_encoder.fit_transform(data['online_order'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a7287138-43c4-4dd4-97fe-1ff241b584ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    16387\n",
       "0     6872\n",
       "Name: online_order, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['online_order'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d635b2e5-cf65-4797-aae2-b17d1b3fb803",
   "metadata": {},
   "source": [
    "##### Change string categorical into a categorical int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "8a5db500-12fc-4b34-b4f3-37cbdcd96d60",
   "metadata": {
    "id": "8a5db500-12fc-4b34-b4f3-37cbdcd96d60"
   },
   "outputs": [],
   "source": [
    "# Convert 'book_table' column\n",
    "data['book_table'] = label_encoder.fit_transform(data['book_table'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "fc135839-e047-4ecc-9312-3e87fac8f503",
   "metadata": {
    "id": "fc135839-e047-4ecc-9312-3e87fac8f503",
    "outputId": "39c9cd5e-28af-4085-dc88-c4f61665acc1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    17198\n",
       "1     6061\n",
       "Name: book_table, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['book_table'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ff5458-5020-42f1-9370-0ac204d7d032",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### label encode the categorical variables to make it easier to build algorithm\n",
    "- location\n",
    "- rest_type\n",
    "- cuisines"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72be8a0d-b27f-44c3-bfea-6f89671b4483",
   "metadata": {},
   "source": [
    "Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f3442624-69f1-46f6-9e47-378e8b4ff193",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the LabelEncoder\n",
    "location_encoder = LabelEncoder()\n",
    "\n",
    "# Fit and transform the LabelEncoder\n",
    "location_encoder.fit(data['location'])\n",
    "data['location'] = location_encoder.transform(data['location'])\n",
    "\n",
    "# Save the label encoder\n",
    "with open('location_encoder.pickle', 'wb') as handle:\n",
    "    pickle.dump(location_encoder, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10eb33ea-3e26-4c6f-b7be-67535fe47a37",
   "metadata": {},
   "source": [
    "rest_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3dd78000-1aab-42eb-9cfd-51c37f693ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the LabelEncoder\n",
    "RestType_encoder = LabelEncoder()\n",
    "\n",
    "# Fit and transform the LabelEncoder\n",
    "RestType_encoder.fit(data['rest_type'])\n",
    "data['rest_type'] = RestType_encoder.transform(data['rest_type'])\n",
    "\n",
    "# Save the label encoder\n",
    "with open('RestType_encoder.pickle', 'wb') as handle:\n",
    "    pickle.dump(RestType_encoder, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f6b023-6dd8-4fb9-abc0-97bbe904e71d",
   "metadata": {},
   "source": [
    "cuisines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b046cd5d-0fe9-4dd3-93dc-39abe82059d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the LabelEncoder\n",
    "cuisines_encoder = LabelEncoder()\n",
    "\n",
    "# Fit and transform the LabelEncoder\n",
    "cuisines_encoder.fit(data['cuisines'])\n",
    "data['cuisines'] = cuisines_encoder.transform(data['cuisines'])\n",
    "\n",
    "# Save the label encoder\n",
    "with open('cuisines_encoder.pickle', 'wb') as handle:\n",
    "    pickle.dump(cuisines_encoder, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9abed59b-116a-4899-b8e3-c15ad198dca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Load the label encoder\n",
    "# with open('location_encoder.pickle', 'rb') as l_handle:\n",
    "#     location_encoder = pickle.load(l_handle)\n",
    "\n",
    "# # Load the label encoder\n",
    "# with open('RestType_encoder.pickle', 'rb') as l_handle:\n",
    "#     RestType_encoder = pickle.load(l_handle)\n",
    "    \n",
    "# # Load the label encoder\n",
    "# with open('cuisines_encoder.pickle', 'rb') as l_handle:\n",
    "#     cuisines_encoder = pickle.load(l_handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a58c13ef-7c5a-46f4-a12e-5165a1299c6c",
   "metadata": {
    "id": "a58c13ef-7c5a-46f4-a12e-5165a1299c6c",
    "outputId": "799d77bc-2c79-4b7c-87ef-d25422817463"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>online_order</th>\n",
       "      <th>book_table</th>\n",
       "      <th>rate</th>\n",
       "      <th>votes</th>\n",
       "      <th>location</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>cost_for_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>775</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1386</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>787</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>594</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>918</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>484</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   online_order  book_table  rate  votes  location  rest_type  cuisines  \\\n",
       "0             1           1   4.1    775         1         20      1386   \n",
       "1             1           0   4.1    787         1         20       594   \n",
       "2             1           0   3.8    918         1         16       484   \n",
       "\n",
       "   cost_for_2  \n",
       "0       800.0  \n",
       "1       800.0  \n",
       "2       800.0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b83bab3-cfb1-4e0e-8bf9-41fff2aa9260",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Splitting data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c19b85c0-274d-4465-b5a9-9834472581f5",
   "metadata": {},
   "source": [
    "##### Split data into features(X) and targets(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "28b247f0-a114-40b5-8eb1-c9d7fd553356",
   "metadata": {
    "id": "28b247f0-a114-40b5-8eb1-c9d7fd553356",
    "outputId": "fb23b6f5-6fcb-4cb0-c80f-3128889d3fcc"
   },
   "outputs": [],
   "source": [
    "X = data.drop(['rate'], axis=1)\n",
    "Y = data['rate']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74faa558-c472-447c-ad4f-7be24715efd6",
   "metadata": {},
   "source": [
    "##### split data into training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4ce77cf9-28c6-4d35-8446-810b010486e2",
   "metadata": {
    "id": "4ce77cf9-28c6-4d35-8446-810b010486e2"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size= 0.3, random_state = rs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2b5520-22b1-4d18-99b8-63cbd1103a2f",
   "metadata": {},
   "source": [
    "### Define Function for plotting results distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "21dc894f-55d4-46ad-b270-f74c4e458977",
   "metadata": {},
   "outputs": [],
   "source": [
    "def distributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
    "    fig = go.Figure()\n",
    "\n",
    "    fig.add_trace(go.Histogram(x=RedFunction, histnorm='probability', name=RedName, marker_color='red'))\n",
    "    fig.add_trace(go.Histogram(x=BlueFunction, histnorm='probability', name=BlueName, marker_color='blue'))\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=Title,\n",
    "        xaxis_title='ratings',\n",
    "        yaxis_title='count',\n",
    "        barmode='overlay'\n",
    "    )\n",
    "\n",
    "    fig.show(renderer='png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20803a3a-937d-4008-b96b-3bcf1554873e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4455c1bc-97b5-4170-ad70-7b5259014b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5d436952-fdd6-4a7c-b277-f398edf782c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22007355129665584"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)\n",
    "lr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd26e142-0b94-4726-b9d8-0361eefb02bc",
   "metadata": {},
   "source": [
    "**INSIGHT:**\n",
    "- We can see that a simple linear regression model do not fit well with this problem so we try other advanced model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce53abc7-51f9-4807-9ef8-d4023b9bc8c6",
   "metadata": {
    "id": "ce53abc7-51f9-4807-9ef8-d4023b9bc8c6",
    "tags": []
   },
   "source": [
    "### Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "14d0c592-786d-4068-acac-b98c679518fa",
   "metadata": {
    "id": "14d0c592-786d-4068-acac-b98c679518fa",
    "outputId": "a26448a0-6e48-43d6-ad89-9d04e1ce84fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score: 0.8869\n"
     ]
    }
   ],
   "source": [
    "# Instantiate model\n",
    "RF_model = RandomForestRegressor(n_estimators= 650,\n",
    "                                 random_state= rs, min_samples_leaf = 0.0001)\n",
    "# Train model\n",
    "RF_model.fit(x_train, y_train)\n",
    "# # Make prediction\n",
    "y_predict = RF_model.predict(x_test)\n",
    "# score model\n",
    "rf_score = round(r2_score(y_test, y_predict), 4)\n",
    "print('r2 score: {}'.format(rf_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ef3c2e40-6693-4218-a54f-ee465391ca22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize results\n",
    "Title = \"Distribution Plot of Predicted Rating Using y_test and y_predict\"\n",
    "distributionPlot(y_test, y_predict, \"Actual Ratings\", \"Predicted Ratings\", Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f107d74b-efa7-4aab-bb4d-1ef8c6f676e8",
   "metadata": {
    "id": "f107d74b-efa7-4aab-bb4d-1ef8c6f676e8"
   },
   "source": [
    "### Extra Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "eac75509-53b8-4795-a5c1-01eebe216738",
   "metadata": {
    "id": "eac75509-53b8-4795-a5c1-01eebe216738",
    "outputId": "dc0fcdd9-8a93-4fe8-92c5-55c8b1d60b5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score: 0.9388\n"
     ]
    }
   ],
   "source": [
    "# Instantiate model\n",
    "ET_model = ExtraTreesRegressor(n_estimators= 120)\n",
    "# Train model\n",
    "ET_model.fit(x_train, y_train)\n",
    "# Make prediction\n",
    "y_predict = ET_model.predict(x_test)\n",
    "# score model\n",
    "ext_score = round(r2_score(y_test, y_predict), 4)\n",
    "print('r2 score: {}'.format(ext_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "eaebd3db-1e74-4ad5-b1e0-f7b5bce3de51",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize results\n",
    "Title = \"Distribution Plot of Predicted Rating Using y_test and y_predict\"\n",
    "distributionPlot(y_test, y_predict, \"Actual Ratings\", \"Predicted Ratings\", Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00a1c282-5e2c-44b8-a569-47762045d0dd",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "23c706af-6d81-4388-a42f-b04f9efe1ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r2_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ExtraTrees</th>\n",
       "      <td>0.9388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest</th>\n",
       "      <td>0.8869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              r2_score\n",
       "ExtraTrees      0.9388\n",
       "RandomForest    0.8869"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = [[rf_score], [ext_score]]\n",
    "index = ['RandomForest', 'ExtraTrees']\n",
    "columns = ['r2_score']\n",
    "result_df = pd.DataFrame(row, columns =columns, index=index)\n",
    "result_df = result_df.sort_values(by='r2_score', ascending=False)\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbe7a19-c8e2-449d-af9c-5ce106049a9e",
   "metadata": {},
   "source": [
    "##### Visualizing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "def19e69-e545-4fcf-b372-d45e089adeba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(result_df,\n",
    "             y='r2_score',\n",
    "             x=result_df.index,\n",
    "             color=result_df.index,\n",
    "             template = 'ggplot2',\n",
    "             height = 300,\n",
    "            width = 500\n",
    "            )\n",
    "\n",
    "fig.update_layout(\n",
    "    title = 'Models vs r2 score',\n",
    "    yaxis_title = 'R2 Score',\n",
    "    xaxis_title = 'Models'\n",
    ")\n",
    "   \n",
    "fig.show(renderer='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00ddbf7-446a-4464-8447-9c5d0fcb4389",
   "metadata": {
    "id": "e00ddbf7-446a-4464-8447-9c5d0fcb4389"
   },
   "source": [
    "### Save our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "28adb52f-fc56-449d-bab6-4102c85a3696",
   "metadata": {
    "id": "28adb52f-fc56-449d-bab6-4102c85a3696"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(ET_model, open('model.pkl','wb'))\n",
    "# model = pickle.load(open('model.pkl','rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "934ab4a5-d3c1-47a8-bf82-202e16671492",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a id='conclusion'></a>\n",
    "### Conclusion\n",
    "> - The Extra Tree Regressor produced the best result with an `r2 score` of `9396`\n",
    "\n",
    "<a id='limitation'></a>\n",
    "### Limitations\n",
    "> - The dataset contained null values which was dropped during the cleaning procedure\n",
    "> - Our visualization and analysis is limited to only the provided dataset.\n",
    "\n",
    "### Appendix\n",
    "> - Cafe Coffee Day, Onesta and Empire Restaurant are the top 3 most popular restaurants in Bangalore\n",
    "> - Most restaurant do not offer the option of booking a table.\n",
    "> - Most restaurant offers online order and delivery\n",
    "> - Most ratings are between 3.0 and 4.5\n",
    "> - Here the two main service types are Delivery and Dine-out\n",
    "> - Majority of restaurants charges less than 2000 for two people\n",
    "> - Five most liked dishes are Pasta, Pizza, Cocktails, Burger, Mocktails.\n",
    "> - The 3 most common type of restaurants in Bangalore are Casual Dining, Quick Bites and Cafe."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "Zomato_Bangalore_Restaurants.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}