Notebooks/Zomato_Bangalore_Restaurants.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zomato Bangalore Restaurants\n",
    "#### Marks secured by the students in various subjects\n",
    "\n",
    "\n",
    "\n",
    "## Inspiration\n",
    "#### Understand the influence of various factors on the rating for the restaurant.\n",
    "#### Build a model that tries to predict the rating of a restaurant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "import xgboost as xgb\n",
    "from statsmodels.formula.api import ols\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../Datasets/zomato/zomato.csv\", thousands = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>800.0</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>800.0</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>800.0</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>300.0</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>600.0</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",
       "  </tbody>\n",
       "</table>\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",
       "                                             address                   name  \\\n",
       "0  942, 21st Main Road, 2nd Stage, Banashankari, ...                  Jalsa   \n",
       "1  2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...         Spice Elephant   \n",
       "2  1112, Next to KIMS Medical College, 17th Cross...        San Churro Cafe   \n",
       "3  1st Floor, Annakuteera, 3rd Stage, Banashankar...  Addhuri Udupi Bhojana   \n",
       "4  10, 3rd Floor, Lakshmi Associates, Gandhi Baza...          Grand Village   \n",
       "\n",
       "  online_order book_table   rate  votes                             phone  \\\n",
       "0          Yes        Yes  4.1/5    775    080 42297555\\r\\n+91 9743772233   \n",
       "1          Yes         No  4.1/5    787                      080 41714161   \n",
       "2          Yes         No  3.8/5    918                    +91 9663487993   \n",
       "3           No         No  3.7/5     88                    +91 9620009302   \n",
       "4           No         No  3.8/5    166  +91 8026612447\\r\\n+91 9901210005   \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",
       "                                          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",
       "                         cuisines  approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                        800.0   \n",
       "1     Chinese, North Indian, Thai                        800.0   \n",
       "2          Cafe, Mexican, Italian                        800.0   \n",
       "3      South Indian, North Indian                        300.0   \n",
       "4        North Indian, Rajasthani                        600.0   \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",
       "  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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: 51717 rows x 17 columns\n"
     ]
    }
   ],
   "source": [
    "print(f\"Shape: {df.shape[0]} rows x {df.shape[1]} columns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the overall description of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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>count</th>\n",
       "      <td>51717</td>\n",
       "      <td>51717</td>\n",
       "      <td>51717</td>\n",
       "      <td>51717</td>\n",
       "      <td>51717</td>\n",
       "      <td>43942</td>\n",
       "      <td>51717.000000</td>\n",
       "      <td>50509</td>\n",
       "      <td>51696</td>\n",
       "      <td>51490</td>\n",
       "      <td>23639</td>\n",
       "      <td>51672</td>\n",
       "      <td>51371.000000</td>\n",
       "      <td>51717</td>\n",
       "      <td>51717</td>\n",
       "      <td>51717</td>\n",
       "      <td>51717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>51717</td>\n",
       "      <td>11495</td>\n",
       "      <td>8792</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14926</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>5271</td>\n",
       "      <td>2723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22513</td>\n",
       "      <td>9098</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>https://www.zomato.com/bangalore/shan-restaura...</td>\n",
       "      <td>Delivery Only</td>\n",
       "      <td>Cafe Coffee Day</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NEW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>080 43334321</td>\n",
       "      <td>BTM</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>BTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>96</td>\n",
       "      <td>30444</td>\n",
       "      <td>45268</td>\n",
       "      <td>2208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216</td>\n",
       "      <td>5124</td>\n",
       "      <td>19132</td>\n",
       "      <td>182</td>\n",
       "      <td>2913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7595</td>\n",
       "      <td>39617</td>\n",
       "      <td>25942</td>\n",
       "      <td>3279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>283.697527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>555.431566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>803.838853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>438.850728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16832.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      url        address  \\\n",
       "count                                               51717          51717   \n",
       "unique                                              51717          11495   \n",
       "top     https://www.zomato.com/bangalore/shan-restaura...  Delivery Only   \n",
       "freq                                                    1            128   \n",
       "mean                                                  NaN            NaN   \n",
       "std                                                   NaN            NaN   \n",
       "min                                                   NaN            NaN   \n",
       "25%                                                   NaN            NaN   \n",
       "50%                                                   NaN            NaN   \n",
       "75%                                                   NaN            NaN   \n",
       "max                                                   NaN            NaN   \n",
       "\n",
       "                   name online_order book_table   rate         votes  \\\n",
       "count             51717        51717      51717  43942  51717.000000   \n",
       "unique             8792            2          2     64           NaN   \n",
       "top     Cafe Coffee Day          Yes         No    NEW           NaN   \n",
       "freq                 96        30444      45268   2208           NaN   \n",
       "mean                NaN          NaN        NaN    NaN    283.697527   \n",
       "std                 NaN          NaN        NaN    NaN    803.838853   \n",
       "min                 NaN          NaN        NaN    NaN      0.000000   \n",
       "25%                 NaN          NaN        NaN    NaN      7.000000   \n",
       "50%                 NaN          NaN        NaN    NaN     41.000000   \n",
       "75%                 NaN          NaN        NaN    NaN    198.000000   \n",
       "max                 NaN          NaN        NaN    NaN  16832.000000   \n",
       "\n",
       "               phone location    rest_type dish_liked      cuisines  \\\n",
       "count          50509    51696        51490      23639         51672   \n",
       "unique         14926       93           93       5271          2723   \n",
       "top     080 43334321      BTM  Quick Bites    Biryani  North Indian   \n",
       "freq             216     5124        19132        182          2913   \n",
       "mean             NaN      NaN          NaN        NaN           NaN   \n",
       "std              NaN      NaN          NaN        NaN           NaN   \n",
       "min              NaN      NaN          NaN        NaN           NaN   \n",
       "25%              NaN      NaN          NaN        NaN           NaN   \n",
       "50%              NaN      NaN          NaN        NaN           NaN   \n",
       "75%              NaN      NaN          NaN        NaN           NaN   \n",
       "max              NaN      NaN          NaN        NaN           NaN   \n",
       "\n",
       "        approx_cost(for two people) reviews_list menu_item listed_in(type)  \\\n",
       "count                  51371.000000        51717     51717           51717   \n",
       "unique                          NaN        22513      9098               7   \n",
       "top                             NaN           []        []        Delivery   \n",
       "freq                            NaN         7595     39617           25942   \n",
       "mean                     555.431566          NaN       NaN             NaN   \n",
       "std                      438.850728          NaN       NaN             NaN   \n",
       "min                       40.000000          NaN       NaN             NaN   \n",
       "25%                      300.000000          NaN       NaN             NaN   \n",
       "50%                      400.000000          NaN       NaN             NaN   \n",
       "75%                      650.000000          NaN       NaN             NaN   \n",
       "max                     6000.000000          NaN       NaN             NaN   \n",
       "\n",
       "       listed_in(city)  \n",
       "count            51717  \n",
       "unique              30  \n",
       "top                BTM  \n",
       "freq              3279  \n",
       "mean               NaN  \n",
       "std                NaN  \n",
       "min                NaN  \n",
       "25%                NaN  \n",
       "50%                NaN  \n",
       "75%                NaN  \n",
       "max                NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the number of NA in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will need to keep these in mind when dealing with the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                             object\n",
       "address                         object\n",
       "name                            object\n",
       "online_order                    object\n",
       "book_table                      object\n",
       "rate                            object\n",
       "votes                            int64\n",
       "phone                           object\n",
       "location                        object\n",
       "rest_type                       object\n",
       "dish_liked                      object\n",
       "cuisines                        object\n",
       "approx_cost(for two people)    float64\n",
       "reviews_list                    object\n",
       "menu_item                       object\n",
       "listed_in(type)                 object\n",
       "listed_in(city)                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall it fits the data in each column with the exception of the rate. Let's start transforming and analysing this data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **EDA**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some columns that will not be used during this work, let's drop these in order to facilitate its analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</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>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>775</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4.1/5</td>\n",
       "      <td>787</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>918</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.7/5</td>\n",
       "      <td>88</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.8/5</td>\n",
       "      <td>166</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  online_order book_table   rate  votes            rest_type  \\\n",
       "0          Yes        Yes  4.1/5    775        Casual Dining   \n",
       "1          Yes         No  4.1/5    787        Casual Dining   \n",
       "2          Yes         No  3.8/5    918  Cafe, Casual Dining   \n",
       "3           No         No  3.7/5     88          Quick Bites   \n",
       "4           No         No  3.8/5    166        Casual Dining   \n",
       "\n",
       "                         cuisines  approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                        800.0   \n",
       "1     Chinese, North Indian, Thai                        800.0   \n",
       "2          Cafe, Mexican, Italian                        800.0   \n",
       "3      South Indian, North Indian                        300.0   \n",
       "4        North Indian, Rajasthani                        600.0   \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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_keep = [\"online_order\", \"book_table\", \"rate\", \"votes\", \"rest_type\", \"cuisines\", \"approx_cost(for two people)\", \"listed_in(type)\", \"listed_in(city)\"]\n",
    "df = df[to_keep].copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change the rate. We will not only eliminate the \"/5\" but also divide every number by 5 in order to keep the rate between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rate.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to drop the rows with \"NEW\", \"-\" and nan values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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', 'nan', '2.9/5', '3.5/5', '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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rate'] = df['rate'].astype(str)\n",
    "df['rate'] = df['rate'].apply(lambda x: x.replace('NEW', str(np.nan)))\n",
    "df['rate'] = df['rate'].apply(lambda x: x.replace('-', str(np.nan)))\n",
    "df['rate'] = df['rate'].apply(lambda x: x.replace('nan', str(np.nan)))\n",
    "df.rate.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_rate(value):\n",
    "    return float((value)[:3])/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[\"transformed_rate\"] = df[\"rate\"].apply(get_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=['transformed_rate'], inplace=True)\n",
    "df.transformed_rate.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82, 0.76, 0.74, 0.72, 0.92, 0.8 , 0.84, 0.78, 0.62, 0.6 , 0.64,\n",
       "       0.66, 0.56, 0.88, 0.86, 0.58, 0.7 , 0.52, 0.68, 0.9 , 0.5 , 0.54,\n",
       "       0.94, 0.48, 0.44, 0.46, 0.96, 0.98, 0.42, 0.4 , 0.36])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.transformed_rate.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"rate\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check what other columns need to be transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "online_order                     0\n",
       "book_table                       0\n",
       "votes                            0\n",
       "rest_type                      149\n",
       "cuisines                        11\n",
       "approx_cost(for two people)    247\n",
       "listed_in(type)                  0\n",
       "listed_in(city)                  0\n",
       "transformed_rate                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's deal with \"rest_type\" next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>votes</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mughlai, North Indian, Chinese, Momos</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fast Food, Finger Food</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Indian, Chinese, Arabian</td>\n",
       "      <td>700.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biryani, North Indian</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biryani, North Indian</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     online_order book_table  votes rest_type  \\\n",
       "999           Yes         No    269       NaN   \n",
       "1300          Yes         No      7       NaN   \n",
       "1376          Yes         No     15       NaN   \n",
       "1637           No         No    392       NaN   \n",
       "2044           No         No    392       NaN   \n",
       "\n",
       "                                   cuisines  approx_cost(for two people)  \\\n",
       "999   Mughlai, North Indian, Chinese, Momos                        600.0   \n",
       "1300                 Fast Food, Finger Food                        300.0   \n",
       "1376         North Indian, Chinese, Arabian                        700.0   \n",
       "1637                  Biryani, North Indian                        500.0   \n",
       "2044                  Biryani, North Indian                        500.0   \n",
       "\n",
       "     listed_in(type)    listed_in(city)  transformed_rate  \n",
       "999         Delivery  Bannerghatta Road              0.70  \n",
       "1300        Delivery  Bannerghatta Road              0.60  \n",
       "1376        Delivery  Bannerghatta Road              0.72  \n",
       "1637        Delivery  Bannerghatta Road              0.48  \n",
       "2044        Dine-out  Bannerghatta Road              0.48  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.rest_type.isna()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>votes</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>254</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Biryani, North Indian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>128</td>\n",
       "      <td>Takeaway, Delivery</td>\n",
       "      <td>Biryani, North Indian</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>36</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Biryani, North Indian</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Biryani, North Indian</td>\n",
       "      <td>700.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>181</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Biryani, North Indian</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bannerghatta Road</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     online_order book_table  votes           rest_type  \\\n",
       "924           Yes         No    254       Casual Dining   \n",
       "986           Yes         No    128  Takeaway, Delivery   \n",
       "1093          Yes         No     36            Delivery   \n",
       "1105          Yes         No     45            Delivery   \n",
       "1113          Yes         No    181            Delivery   \n",
       "\n",
       "                   cuisines  approx_cost(for two people) listed_in(type)  \\\n",
       "924   Biryani, North Indian                        800.0        Delivery   \n",
       "986   Biryani, North Indian                        400.0        Delivery   \n",
       "1093  Biryani, North Indian                        600.0        Delivery   \n",
       "1105  Biryani, North Indian                        700.0        Delivery   \n",
       "1113  Biryani, North Indian                        500.0        Delivery   \n",
       "\n",
       "        listed_in(city)  transformed_rate  \n",
       "924   Bannerghatta Road              0.76  \n",
       "986   Bannerghatta Road              0.74  \n",
       "1093  Bannerghatta Road              0.74  \n",
       "1105  Bannerghatta Road              0.60  \n",
       "1113  Bannerghatta Road              0.74  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.cuisines == \"Biryani, North Indian\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no apparent logic that could be applied to assign a \"rest_type\" to the NaN so we will just drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['rest_type'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "online_order                     0\n",
       "book_table                       0\n",
       "votes                            0\n",
       "rest_type                        0\n",
       "cuisines                        11\n",
       "approx_cost(for two people)    245\n",
       "listed_in(type)                  0\n",
       "listed_in(city)                  0\n",
       "transformed_rate                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's deal with \"cuisines\" now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>votes</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Beverage Shop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6887</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>9</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Brookefield</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22236</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Beverage Shop</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>JP Nagar</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24725</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Kalyan Nagar</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26186</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Kammanahalli</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      online_order book_table  votes      rest_type cuisines  \\\n",
       "440            Yes         No      4  Beverage Shop      NaN   \n",
       "6887           Yes         No      9       Delivery      NaN   \n",
       "22236          Yes         No      4  Beverage Shop      NaN   \n",
       "24725          Yes         No     24  Casual Dining      NaN   \n",
       "26186          Yes         No     24  Casual Dining      NaN   \n",
       "\n",
       "       approx_cost(for two people) listed_in(type) listed_in(city)  \\\n",
       "440                          100.0        Delivery    Banashankari   \n",
       "6887                         400.0        Delivery     Brookefield   \n",
       "22236                        100.0        Delivery        JP Nagar   \n",
       "24725                        600.0        Dine-out    Kalyan Nagar   \n",
       "26186                        600.0        Dine-out    Kammanahalli   \n",
       "\n",
       "       transformed_rate  \n",
       "440                0.66  \n",
       "6887               0.72  \n",
       "22236              0.66  \n",
       "24725              0.66  \n",
       "26186              0.66  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.cuisines.isna()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>votes</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>506</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>North Indian, Fast Food, Chinese, Burger</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bakery, Desserts</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>172</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>750.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>415</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>North Indian, Biryani, Fast Food</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>230</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>650.0</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   online_order book_table  votes    rest_type  \\\n",
       "31          Yes         No    506  Quick Bites   \n",
       "32          Yes         No     35     Delivery   \n",
       "33          Yes         No    172     Delivery   \n",
       "34          Yes         No    415  Quick Bites   \n",
       "35          Yes         No    230     Delivery   \n",
       "\n",
       "                                    cuisines  approx_cost(for two people)  \\\n",
       "31  North Indian, Fast Food, Chinese, Burger                        500.0   \n",
       "32                          Bakery, Desserts                        500.0   \n",
       "33                                     Pizza                        750.0   \n",
       "34          North Indian, Biryani, Fast Food                        500.0   \n",
       "35                                   Biryani                        650.0   \n",
       "\n",
       "   listed_in(type) listed_in(city)  transformed_rate  \n",
       "31        Delivery    Banashankari              0.56  \n",
       "32        Delivery    Banashankari              0.78  \n",
       "33        Delivery    Banashankari              0.78  \n",
       "34        Delivery    Banashankari              0.84  \n",
       "35        Delivery    Banashankari              0.78  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"listed_in(type)\"] == \"Delivery\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, no apparent logic can be applied and, as such, we will also drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['cuisines'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only column left with NA is \"approx_cost(for two people)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>votes</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3774</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>237</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Bellandur</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3954</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>Takeaway</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bellandur</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4040</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>Takeaway, Delivery</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>Bellandur</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4852</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>237</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dine-out</td>\n",
       "      <td>Bellandur</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4952</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>237</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Finger Food</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Drinks &amp; nightlife</td>\n",
       "      <td>Bellandur</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     online_order book_table  votes           rest_type      cuisines  \\\n",
       "3774           No         No    237                 Bar   Finger Food   \n",
       "3954           No         No     13            Takeaway  North Indian   \n",
       "4040           No         No      6  Takeaway, Delivery  North Indian   \n",
       "4852           No         No    237                 Bar   Finger Food   \n",
       "4952           No         No    237                 Bar   Finger Food   \n",
       "\n",
       "      approx_cost(for two people)     listed_in(type) listed_in(city)  \\\n",
       "3774                          NaN              Buffet       Bellandur   \n",
       "3954                          NaN            Delivery       Bellandur   \n",
       "4040                          NaN            Delivery       Bellandur   \n",
       "4852                          NaN            Dine-out       Bellandur   \n",
       "4952                          NaN  Drinks & nightlife       Bellandur   \n",
       "\n",
       "      transformed_rate  \n",
       "3774              0.78  \n",
       "3954              0.64  \n",
       "4040              0.66  \n",
       "4852              0.78  \n",
       "4952              0.78  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"approx_cost(for two people)\"].isna()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we could use the mean of restaurants with the same type and city to assign values to these rows but, giving its low percentage when compared with the entire dataset, it's better to also just drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=[\"approx_cost(for two people)\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "online_order                   0\n",
       "book_table                     0\n",
       "votes                          0\n",
       "rest_type                      0\n",
       "cuisines                       0\n",
       "approx_cost(for two people)    0\n",
       "listed_in(type)                0\n",
       "listed_in(city)                0\n",
       "transformed_rate               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have any *na* values left. Let's check how many rows we lost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10454 rows were dropped\n"
     ]
    }
   ],
   "source": [
    "print(f\"{51717 - df.shape[0]} rows were dropped\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start to try to understand what factors can affect the rate of a restaurant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's check the distribution of the rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>votes</th>\n",
       "      <th>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41263</td>\n",
       "      <td>41263</td>\n",
       "      <td>41263.000000</td>\n",
       "      <td>41263</td>\n",
       "      <td>41263</td>\n",
       "      <td>41263.000000</td>\n",
       "      <td>41263</td>\n",
       "      <td>41263</td>\n",
       "      <td>41263.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>2367</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>North Indian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delivery</td>\n",
       "      <td>BTM</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>27102</td>\n",
       "      <td>34959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13885</td>\n",
       "      <td>2108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20452</td>\n",
       "      <td>2586</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>352.683179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>603.914063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.740402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>884.177761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>464.942973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16832.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       online_order book_table         votes    rest_type      cuisines  \\\n",
       "count         41263      41263  41263.000000        41263         41263   \n",
       "unique            2          2           NaN           87          2367   \n",
       "top             Yes         No           NaN  Quick Bites  North Indian   \n",
       "freq          27102      34959           NaN        13885          2108   \n",
       "mean            NaN        NaN    352.683179          NaN           NaN   \n",
       "std             NaN        NaN    884.177761          NaN           NaN   \n",
       "min             NaN        NaN      0.000000          NaN           NaN   \n",
       "25%             NaN        NaN     21.000000          NaN           NaN   \n",
       "50%             NaN        NaN     73.000000          NaN           NaN   \n",
       "75%             NaN        NaN    277.000000          NaN           NaN   \n",
       "max             NaN        NaN  16832.000000          NaN           NaN   \n",
       "\n",
       "        approx_cost(for two people) listed_in(type) listed_in(city)  \\\n",
       "count                  41263.000000           41263           41263   \n",
       "unique                          NaN               7              30   \n",
       "top                             NaN        Delivery             BTM   \n",
       "freq                            NaN           20452            2586   \n",
       "mean                     603.914063             NaN             NaN   \n",
       "std                      464.942973             NaN             NaN   \n",
       "min                       40.000000             NaN             NaN   \n",
       "25%                      300.000000             NaN             NaN   \n",
       "50%                      500.000000             NaN             NaN   \n",
       "75%                      750.000000             NaN             NaN   \n",
       "max                     6000.000000             NaN             NaN   \n",
       "\n",
       "        transformed_rate  \n",
       "count       41263.000000  \n",
       "unique               NaN  \n",
       "top                  NaN  \n",
       "freq                 NaN  \n",
       "mean            0.740402  \n",
       "std             0.087997  \n",
       "min             0.360000  \n",
       "25%             0.680000  \n",
       "50%             0.740000  \n",
       "75%             0.800000  \n",
       "max             0.980000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b49309a8d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.transformed_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have a peak between 0.7 and 0.8. This is expected given the mean of 0.74.\n",
    "\n",
    "Let's see if there is any correlation between our numeric values: number of votes, cost and rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1b493083a90>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>votes</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>votes</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.366556</td>\n",
       "      <td>0.435256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <td>0.366556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.385384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transformed_rate</th>\n",
       "      <td>0.435256</td>\n",
       "      <td>0.385384</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                votes  approx_cost(for two people)  \\\n",
       "votes                        1.000000                     0.366556   \n",
       "approx_cost(for two people)  0.366556                     1.000000   \n",
       "transformed_rate             0.435256                     0.385384   \n",
       "\n",
       "                             transformed_rate  \n",
       "votes                                0.435256  \n",
       "approx_cost(for two people)          0.385384  \n",
       "transformed_rate                     1.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it is possible to see, there only seems to exist a weak correlation between all values, with the strongest one being between the rate and the number of votes.\n",
    "This could imply a small tendency for people to want to vote when they had a very good experience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if our other labels have some effect on this rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Online Order**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rating by online order possibility')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "s0 = sns.countplot(df.online_order, ax=axs[0])\n",
    "s0.set_title(\"Counts\")\n",
    "s1 = sns.barplot(x=\"online_order\", y = \"transformed_rate\", data=df, ax=axs[1])\n",
    "s1.set_ylim(0.6, 1)\n",
    "s1.set_title(\"Rating by online order possibility\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the rating of a restaurant appears to be affected by the possibility of ordering online, even if the difference is very small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Book table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rating by book table service')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "s0 = sns.countplot(df.book_table, ax=axs[0])\n",
    "s0.set_title(\"Counts\")\n",
    "s1 = sns.barplot(x=\"book_table\", y = \"transformed_rate\", data=df, ax=axs[1])\n",
    "s1.set_ylim(0.6, 1)\n",
    "s1.set_title(\"Rating by book table service\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is quite a big difference between the rating of restaurants that have table reservation and those who do not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Buffet'),\n",
       " Text(0, 0, 'Cafes'),\n",
       " Text(0, 0, 'Delivery'),\n",
       " Text(0, 0, 'Desserts'),\n",
       " Text(0, 0, 'Dine-out'),\n",
       " Text(0, 0, 'Drinks & nightlife'),\n",
       " Text(0, 0, 'Pubs and bars')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "s0 = sns.countplot(df[\"listed_in(type)\"], ax=axs[0])\n",
    "s0.set_title(\"Counts\")\n",
    "s1 = sns.barplot(x=\"listed_in(type)\", y = \"transformed_rate\", data=df, ax=axs[1])\n",
    "s1.set_ylim(0.6, 1)\n",
    "s1.set_title(\"Rating by type of restaurant\")\n",
    "s1.set_xticklabels(s1.get_xticklabels(), rotation=90)\n",
    "s0.set_xticklabels(s0.get_xticklabels(), rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a variation between the various types of restaurants, with Buffet, Drinks & Nightlife and Pubs and Bars having the biggest rating and Delivery and Dine-out having the lowest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **City**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Banashankari'),\n",
       " Text(0, 0, 'Bannerghatta Road'),\n",
       " Text(0, 0, 'Basavanagudi'),\n",
       " Text(0, 0, 'Bellandur'),\n",
       " Text(0, 0, 'Brigade Road'),\n",
       " Text(0, 0, 'Brookefield'),\n",
       " Text(0, 0, 'BTM'),\n",
       " Text(0, 0, 'Church Street'),\n",
       " Text(0, 0, 'Electronic City'),\n",
       " Text(0, 0, 'Frazer Town'),\n",
       " Text(0, 0, 'HSR'),\n",
       " Text(0, 0, 'Indiranagar'),\n",
       " Text(0, 0, 'Jayanagar'),\n",
       " Text(0, 0, 'JP Nagar'),\n",
       " Text(0, 0, 'Kalyan Nagar'),\n",
       " Text(0, 0, 'Kammanahalli'),\n",
       " Text(0, 0, 'Koramangala 4th Block'),\n",
       " Text(0, 0, 'Koramangala 5th Block'),\n",
       " Text(0, 0, 'Koramangala 6th Block'),\n",
       " Text(0, 0, 'Koramangala 7th Block'),\n",
       " Text(0, 0, 'Lavelle Road'),\n",
       " Text(0, 0, 'Malleshwaram'),\n",
       " Text(0, 0, 'Marathahalli'),\n",
       " Text(0, 0, 'MG Road'),\n",
       " Text(0, 0, 'New BEL Road'),\n",
       " Text(0, 0, 'Old Airport Road'),\n",
       " Text(0, 0, 'Rajajinagar'),\n",
       " Text(0, 0, 'Residency Road'),\n",
       " Text(0, 0, 'Sarjapur Road'),\n",
       " Text(0, 0, 'Whitefield')]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7kAAAHuCAYAAABas8umAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdebglZXnv/e+PRpShGQwNyCRocECPktjBMUbCUdsBW0ANiEM4aosB0WNOFEzeaEzI69FoFCUSooAkIEEFAW0B9RWJBiONQZDp2AcRmmZoggyNAnZzv39UNay9ew9rd++1115rfz/Xta9a9QxVd+2p1r3qqadSVUiSJEmSNAw26XcAkiRJkiRNF5NcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJUl8lOSzJRT3a9o1J/nsvtj2X9eP7muSDST4/k/vUYDLJlQZQkjcmWZZkdZJbk3wzyYt6vM9K8tu93IckaTC0Cc6v2/PQbUlOTbJVl333aM8pm64rq6rTq+plvYt4dkny4ST/0u84xjMb4kvykiQrOsuq6m+r6u39ikmDwyRXGjBJ3gd8CvhbYEdgd+AfgMX9jEuSNOccUFVbAfsAvwMc2+d4ZlRnkj7bJJnX7xikfjLJlQZIkm2AjwBHVtXZVXV/Vf2mqs6vqj9L8tgkn0qysv36VJLHtn3/OMn3R23vkauz7afwJyT5RpL7kvxHkie3dZe0XX7Sfmr/R0m2T/L1JHcnuSvJvyXxf4okzTFVdRtwIU2yC0CSVyX5zyT3Jrk5yYc7uqw7p9zdnlOeP/oc1Z6fjkjysyS/bM9PaevmJflEkjuT/DzJUaOvDI/h95Jc027rlCSPa7f10yQHdOz3Me129xm9gXVXFpN8IMltwClt+auTXNGeD/89ybM6+nwgyS3tefX6JPsnWQR8EPij9vh/0rY9PMm1bdsbkryzYzvdnMM/l2RpkvuB/Sb6GXRcTX9rkpvaY/7ztm7M+CYy0fuPtn5x+z26N8n/bfcx7jEn2RL4JrBzG8PqJDtn1BXmJK9JcnX7vb84ydM76m5M8r+SXJnkniT/uu7nruHnG1JpsDwfeBxwzjj1fw48j+aNxrOBfYG/mML2DwX+CtgOWA4cB1BVL27rn11VW1XVvwJ/CqwAFtBcUf4gUFM5GEnS4EuyK/AKmvPGOvcDbwG2BV4FvCvJa9u6deeUbdtzyqXjbPrVwO/RnM/eALy8LX9Hu799gN8FXjtm75EOa/s/GXgKj54bTwPe1NHulcCtVXXFONvZCXg88ERgSZLfBU4G3gn8FvCPwHlt0vdU4Cjg96pqfrv/G6vqAprRWP/aHv+z223f0R7z1sDhwN+32+/WG2nO2/OB7zPxz2CdFwFPBfYH/jLJ0yeIbyLjvv9Isi/N9/nP2lheDNw40TFX1f00P+OVbQxbVdXKzh0meQrwJeC9NO9FlgLnJ9mso9kbgEXAnsCzgD/u4lg0BExypcHyW8CdVbVmnPrDgI9U1R1VtYomYX3zFLZ/dlX9qN3+6XR8Kj+G3wBPAJ7YXk3+t6oyyZWkueNrSe4DbqZJVj60rqKqLq6qq6rq4aq6kiYZ+YMpbv+jVXV3Vd0EfJdHz0lvAD5dVSuq6pfAR7vY1mer6uaquosmETy0Lf8X4JVJtm7X3wz88wTbeRj4UFU9WFW/pkm4/7Gq/qOq1lbVF4EHaRK+tcBjgb2TPKaqbqyq/zvehqvqG1X1f6vxPeAi4Pe7OLZ1zq2qH7Tf8we6/Bn8VVX9uqp+AvyEJkHdEBO9/3gbcHJVfauN5Zaqum4ajvmPgG+02/0N8HfA5sALOtocX1Ur25/7+Uz8vkZDxCRXGiz/BWw/wZCsnYFfdKz/oi3r1m0dr38FTDSJyMdpPrW/qB1idMwU9iNJGnyvba9QvgR4GrD9uookz03y3SSrktwDHNFZ36Xxzkk70yTW63S+Hk9nm0fOje3VwR8AByfZlubq4ekTbGdVVT3Qsf5E4E/b4bJ3J7kb2A3YuaqW01xl/DBwR5Izk4x7Tk7yiiQ/THML0N00V5Wn8j0b8X3o8mcwlfP+RCZ6/7EbMGZyv5HHPGKfVfUwzfdgl44203V8GjAmudJguRR4gPGHZq2kOeGus3tbBs2wpS3WVSTZaWMCqar7qupPq+pJwAHA+5LsvzHblCQNnvYK3Kk0V9LWOQM4D9itqrYBTgSyrstG7vJWYNeO9d266NPZpvPcCPBFmiHLrwcurapbJtjO6NhvBo6rqm07vraoqi8BVNUZVfUimnNzAf97rO20969+leZ7uGNVbUsz/Hbd96ybc/jo2Cb6GUxmqj+jid5/3EwzTHyELo55shhG7DNJaH7OE/38NEeY5EoDpKruAf4SOCHJa5NskWaSjFck+RjNUKS/SLIgyfZt23UTNPwEeEaSfdqJFz48xd3fDjxp3UqaiTZ+uz2p3EszLGvtRh2gJGlQfQp4aR6dsGk+cFdVPdDek/nGjraraIb9PokNcxbwniS7tFdfP9BFnyOT7Jrk8TRzSPxrR93XaO7tfQ/NvaNT8U/AEe1V0yTZsp3waX6Spyb5wzaZewD4NY+eJ28H9sijEzZuRjO0eRWwJskrgM5HKm3IOXyin8FkRsc3mYnef3wBODzNpFubtD+3pzH5Md8O/FaaSTfHchbwqna7j6GZK+RB4N+ncJwaUia50oCpqk8C76OZ0GEVzSekR9GcpP8GWAZcCVwF/Lgto6r+D83MzN8GfkYzKcVUfBj4Yjsc6w3AXu22VtNcYf6Hqrp4Iw5NkjSg2vswTwP+n7boT4CPtPfs/iVNQrKu7a9o7ov9QXtOed4Ud/dPNPduXgn8J83VvzVM/EHrGW2fG9qvv+mI59c0VxT3BM6eSiBVtYzmvtzPAr+kuY3nj9vqx9LcL3wnzbDZHWgSbIAvt8v/SvLjqroPOJrm+/RLmoT0vI79bMg5fNyfQRdGxNdF+4nef/yIdlIp4B7gezTzeUx2zNfRJM83tL8nI4Z6V9X1NFfgP0PzPT6A5rFWD03hODWk4jwxkiRJGlTtFcATq+qJkzYefxt/CTylqt40aWNJs55XciVJkjQwkmye5JVJNk2yC82szuM9Wq+b7T2eZgbgk6YrRkn9ZZIrSdIclOTkJHck+ek49UlyfJLlSa7sfF5nkkVJrm/rnFldMy00j6j5Jc1w5WtphuNOfUPJO2hu+/lmVV0ybRFK6iuHK0uSNAcleTHNPfWnVdUzx6h/JfBumkd6PJfmuaTPTTIP+D/AS4EVwGXAoVV1zYwFL0nSBLySK0nSHNRetbprgiaLaRLgqqofAtsmeQKwL7C8qm5oJ3g5s20rSdKssGm/A+iV7bffvvbYY49+hyFJGhKXX375nVW1oN9xzKBdaIZxrrOiLRur/LnjbSTJEmAJwJZbbvmcpz3tadMfqSRpThrv3Dy0Se4ee+zBsmXL+h2GJGlIJPlFv2OYYRmjrCYoH1NVnUQ7oc/ChQvLc7MkabqMd24e2iRXkiRtlBXAbh3ruwIrgc3GKZckaVbwnlxJkjSW84C3tLMsPw+4p6pupZloaq8keybZDDikbStJ0qzglVxJkuagJF8CXgJsn2QFzbNGHwNQVScCS2lmVl4O/Ao4vK1bk+Qo4EJgHnByVV094wcgSdI4THIlSZqDqurQSeoLOHKcuqU0SbAkSbOOw5UlSZIkSUPDJFeSJEmSNDRMciVJkiRJQ8MkV5IkSZI0NExyJUmSJElDwyRXkiRJkjQ0THIlSZIkSUPDJFeSJEmSNDRMciVJkiRJQ8MkV5IkSZI0NDbtdwCa3b5yyqKu277u8At6GIkkSZIkTc4ruZIkSZKkoWGSK0mSJEkaGia5kiRJkqShYZIrSZIkSRoaJrmSJEmSpKFhkitJkiRJGho9S3KT7Jbku0muTXJ1kve05R9OckuSK9qvV3b0OTbJ8iTXJ3l5R/lzklzV1h2fJL2KW5IkSZI0uHr5nNw1wJ9W1Y+TzAcuT/Kttu7vq+rvOhsn2Rs4BHgGsDPw7SRPqaq1wOeAJcAPgaXAIuCbPYxdkiRJkjSAenYlt6puraoft6/vA64Fdpmgy2LgzKp6sKp+DiwH9k3yBGDrqrq0qgo4DXhtr+KWJEmSJA2uGbknN8kewO8A/9EWHZXkyiQnJ9muLdsFuLmj24q2bJf29ejysfazJMmyJMtWrVo1jUcgSZIkSRoEPU9yk2wFfBV4b1XdSzP0+MnAPsCtwCfWNR2je01Qvn5h1UlVtbCqFi5YsGCjY5ckSZIkDZaeJrlJHkOT4J5eVWcDVNXtVbW2qh4G/gnYt22+Atito/uuwMq2fNcxyiVJkiRJGqGXsysH+AJwbVV9sqP8CR3NDgR+2r4+DzgkyWOT7AnsBfyoqm4F7kvyvHabbwHO7VXckiRJkqTB1cvZlV8IvBm4KskVbdkHgUOT7EMz5PhG4J0AVXV1krOAa2hmZj6ynVkZ4F3AqcDmNLMqO7OyJEmSJGk9PUtyq+r7jH0/7dIJ+hwHHDdG+TLgmdMXnSRJkiRpGM3I7MqSJEmSJM0Ek1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJGmOSrIoyfVJlic5Zoz67ZKck+TKJD9K8syOuhuTXJXkiiTLZjZySZLG17Pn5EqSpNkryTzgBOClwArgsiTnVdU1Hc0+CFxRVQcmeVrbfv+O+v2q6s4ZC1qSpC54JVeSpLlpX2B5Vd1QVQ8BZwKLR7XZG/gOQFVdB+yRZMeZDVOSpKkxyZUkaW7aBbi5Y31FW9bpJ8BBAEn2BZ4I7NrWFXBRksuTLBlvJ0mWJFmWZNmqVaumLXhJksZjkitJ0tyUMcpq1PpHge2SXAG8G/hPYE1b98Kq+l3gFcCRSV481k6q6qSqWlhVCxcsWDBNoUuSND7vyZU0tF7/1Su7bvvlg5/Vw0ikWWkFsFvH+q7Ays4GVXUvcDhAkgA/b7+oqpXt8o4k59AMf76k92FLkjQxr+RKkjQ3XQbslWTPJJsBhwDndTZIsm1bB/B24JKqujfJlknmt222BF4G/HQGY5ckaVxeyZUkaQ6qqjVJjgIuBOYBJ1fV1UmOaOtPBJ4OnJZkLXAN8La2+47AOc3FXTYFzqiqC2b6GCRJGotJriRJc1RVLQWWjio7seP1pcBeY/S7AXh2zwOUJGkDOFxZkiRJkjQ0THIlSZIkSUPDJFeSJEmSNDRMciVJkiRJQ8MkV5IkSZI0NExyJUmSJElDwyRXkiRJkjQ0THIlSZIkSUPDJFeSJEmSNDQ27XcAkgbTAV85u+u257/uoEdeL/7KBV33O/d1i6YUkyRJkuSVXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQ2LTfAUhSNw786ve7anfOwS/qcSSSJEmazbySK0mSJEkaGia5kiRJkqShYZIrSZIkSRoaJrmSJEmSpKFhkitJkiRJGhomuZIkSZKkoWGSK0nSHJVkUZLrkyxPcswY9dslOSfJlUl+lOSZ3faVJKlfTHIlSZqDkswDTgBeAewNHJpk71HNPghcUVXPAt4CfHoKfSVJ6guTXEmS5qZ9geVVdUNVPQScCSwe1WZv4DsAVXUdsEeSHbvsK0lSX5jkSpI0N+0C3NyxvqIt6/QT4CCAJPsCTwR27bIvbb8lSZYlWbZq1appCl2SpPH1LMlNsluS7ya5NsnVSd7Tlj8+ybeS/KxdbtfR59j23p7rk7y8o/w5Sa5q645Pkl7FLUnSHDHWubRGrX8U2C7JFcC7gf8E1nTZtymsOqmqFlbVwgULFmxMvJIkdaWXV3LXAH9aVU8Hngcc2d6vcwzwnarai2YI1DEAbd0hwDOARcA/tPf8AHwOWALs1X4t6mHckiTNBSuA3TrWdwVWdjaoqnur6vCq2ofmntwFwM+76StJUr/0LMmtqlur6sft6/uAa2mGMi0Gvtg2+yLw2vb1YuDMqnqwqn4OLAf2TfIEYOuqurSqCjito48kSdowlwF7JdkzyWY0HzSf19kgybZtHcDbgUuq6t5u+kqS1C+bzsROkuwB/A7wH8COVXUrNIlwkh3aZrsAP+zotu7+nt+0r0eXj7WfJTRXfNl9992n7wAkSRoyVbUmyVHAhcA84OSqujrJEW39icDTgdOSrAWuAd42Ud9+HIckSaP1PMlNshXwVeC9VXXvBLfTjnd/z5Tu+wFOAli4cOGYbSRJUqOqlgJLR5Wd2PH6UprbhLrqK0nSbNDT2ZWTPIYmwT29qs5ui29vhyDTLu9oy8e7v2dF+3p0uSRJkiRJI/RyduUAXwCurapPdlSdB7y1ff1W4NyO8kOSPDbJnjSfHP+oHdp8X5Lntdt8S0cfSZIkSZIe0cvhyi8E3gxc1T56AOCDNI8jOCvJ24CbgNcDtPcBnUVzz88a4MiqWtv2exdwKrA58M32S5IkSZKkEXqW5FbV9xn7flqA/cfpcxxw3Bjly4BnTl90kiRJkqRh1NN7ciVJkiRJmkkmuZIkSZKkoWGSK0mSJEkaGia5kiRJkqSh0cvZlSUBrzznb7puu/TAv+hhJJIkSdLw80quJEmSJGlomORKkiRJkoaGSa4kSZIkaWiY5EqSJEmShoZJriRJkiRpaJjkSpIkSZKGhkmuJEmSJGlomORKkiRJkoaGSa4kSZIkaWiY5EqSJEmShoZJriRJkiRpaJjkSpIkSZKGhkmuJEmSJGlomORKkiRJkoaGSa4kSZIkaWiY5EqSJEmShoZJriRJkiRpaJjkSpIkSZKGhkmuJEmSJGlomORKkiRJkoaGSa4kSZIkaWiY5EqSJEmShoZJriRJkiRpaJjkSpI0RyVZlOT6JMuTHDNG/TZJzk/ykyRXJzm8o+7GJFcluSLJspmNXJKk8W3a7wAkSdLMSzIPOAF4KbACuCzJeVV1TUezI4FrquqAJAuA65OcXlUPtfX7VdWdMxu5JEkT80quJElz077A8qq6oU1azwQWj2pTwPwkAbYC7gLWzGyYkiRNjUmuJElDIMkTk/z39vXmSeZP0mUX4OaO9RVtWafPAk8HVgJXAe+pqofbugIuSnJ5kiUTxLUkybIky1atWjWFI5IkacOY5EqSNOCSvAP4CvCPbdGuwNcm6zZGWY1afzlwBbAzsA/w2SRbt3UvrKrfBV4BHJnkxWPtpKpOqqqFVbVwwYIFkx+MJEkbySRXkqTBdyTwQuBegKr6GbDDJH1WALt1rO9Kc8W20+HA2dVYDvwceFq7j5Xt8g7gHJrhz5Ik9Z1JriRJg+/BjsmgSLIp61+VHe0yYK8keybZDDgEOG9Um5uA/dtt7gg8FbghyZbrhkMn2RJ4GfDTaTkSSZI2krMrS5I0+L6X5IPA5kleCvwJcP5EHapqTZKjgAuBecDJVXV1kiPa+hOBvwZOTXIVzfDmD1TVnUmeBJzTzEfFpsAZVXVBrw5OkqSpMMmVJGnwHQO8jWZyqHcCS6vqnybrVFVLgaWjyk7seL2S5irt6H43AM/eyJglSeoJk1xJkgbfu6vq08AjiW2S97RlkiTNKSa50iz1qrOP77rtNw46uoeRSBoAbwVGJ7R/PEaZJElDzyRXkqQBleRQ4I3Ankk6J42aD/xXf6KSJKm/THIlSRpc/w7cCmwPfKKj/D7gyr5EJElSn5nkSpI0oKrqF8AvgOf3OxZJkmYLn5MrSdKAS/K8JJclWZ3koSRrk9zb77gkSeoHk1xJkgbfZ4FDgZ8BmwNvBz7T14gkSeoThytLkjQEqmp5knlVtRY4Jcm/9zsmSZL6wSRXkqTB96skmwFXJPkYzWRUW/Y5JkmS+sLhypIkDb4305zTjwLuB3YDDu5rRJIk9YlXciVJGmBJ5gHHVdWbgAeAv+pzSJIk9ZVXciVJGmDtPbgL2uHKkiTNeV7JlSRp8N0I/CDJeTTDlQGoqk/2LSJJkvrEJFeaglece2TXbb+5+IQeRiJJI6xsvzYB5vc5FkmS+qpnSW6Sk4FXA3dU1TPbsg8D7wBWtc0+WFVL27pjgbcBa4Gjq+rCtvw5wKk0z/1bCrynqqpXcUuSNGiqasL7cJN8pqrePVPxSJLUT728knsqzcPpTxtV/vdV9XedBUn2Bg4BngHsDHw7yVPa+4w+BywBfkiT5C4CvtnDuDUNTvniy7pue/hbL+phJJIk4IX9DkCSpJnSsyS3qi5JskeXzRcDZ1bVg8DPkywH9k1yI7B1VV0KkOQ04LWY5Eqaw757+qrJG7X2O2xBDyORJEmaffoxu/JRSa5McnKS7dqyXYCbO9qsaMt2aV+PLh9TkiVJliVZtmpV928CJUmSJEnDYaaT3M8BTwb2AW4FPtGWZ4y2NUH5mKrqpKpaWFULFyzw6oUkSa2xzqeSJA2lGU1yq+r2qlpbVQ8D/wTs21atAHbraLorzSyRK9rXo8slSVL3Pt3vACRJmikz+gihJE+oqlvb1QOBn7avzwPOSPJJmomn9gJ+VFVrk9yX5HnAfwBvAT4zkzHPRpee9Oqu2z5/ydd7GIkkqZ+SnM/EI5xe0y5PnamYJEnqt14+QuhLwEuA7ZOsAD4EvCTJPjQn5BuBdwJU1dVJzgKuAdYAR7YzKwO8i0cfIfRNnHRKkqR11j2t4CBgJ+Bf2vVDac6zkiTNOb2cXfnQMYq/MEH744DjxihfBjxzGkOTJGkoVNX3AJL8dVW9uKPq/CSX9CksSZL6qqskN8l3qmr/ycokaRgcfc7NkzcCjj9wt8kbSTNjQZInVdUNAEn2BJyBUZI0J02Y5CZ5HLAFzZDj7Xh0dsatae6dlSRJ/fc/gYuT3NCu70F7S5AkSXPNZFdy3wm8lyahvZxHk9x7gRN6GJckSepSVV2QZC/gaW3RdVX1YD9jkiSpXyZMcqvq08Cnk7y7qub8rMaSJM1GSbYA3gc8sarekWSvJE+tKqfYlyTNOV3dk1tVn0nyAprhT5t2lJ/Wo7gkSVL3TqEZcfX8dn0F8GXAJFeSNOd0O/HUPwNPBq4A1j3apwCTXEmS+u/JVfVHSQ4FqKpfJ8lknSRJGkbdPkJoIbB3VY37wHlJktQ3DyXZnOYDaJI8GfCeXEnSnNRtkvtTmofM39rDWCRJ0ob5EHABsFuS04EXAn/c14gkSeqTTbpstz1wTZILk5y37quXgUmSpO5U1beAg2gS2y8BC6vq4sn6JVmU5Poky5McM0b9NknOT/KTJFcnObzbvpIk9Uu3V3I/3MsgJEnSRtsFmEdzbn9xEqrq7PEaJ5lH8zjAl9JMVHVZkvOq6pqOZkcC11TVAUkWANe3V4rXdtFXkqS+6HZ25e/1OhBJkrRhkpwMPAu4Gni4LS5g3CQX2BdYXlU3tNs4E1gMdCaqBcxvJ7HaCrgLWAM8t4u+kiT1RbezK99HO5kFsBnwGOD+qtq6V4FJkqSuPa+q9p5in12AmzvWV9Akr50+C5wHrATmA39UVQ8n6aavJEl90dU9uVU1v6q2br8eBxxMc+KTJEn9d2mSqSa5Yz1iaPRTFF5O8/jAnYF9gM8m2brLvs1OkiVJliVZtmrVqimGKEnS1HV7T+4IVfU1J5mQJE2n2z91eddtd3zvc3oYyUD6Ik2iexvNo4MCVFU9a4I+K4DdOtZ3pbli2+lw4KPtIwSXJ/k58LQu+0ITxEnASQALFy70UYSSpJ7rdrjyQR2rm9A8N9cTlSRJs8PJwJuBq3j0ntzJXAbslWRP4BbgEOCNo9rcBOwP/FuSHYGnAjcAd3fRV5Kkvuj2Su4BHa/XADfSTDAhDaTDz1nUddtTDrygh5FI0rS4qaqm9Gi/qlqT5CjgQppZmU+uqquTHNHWnwj8NXBqkqtorg5/oKruBBir7/QdjiRJG67b2ZUPn7yVJEnqk+uSnAGcTzNcGWDCRwi19UuBpaPKTux4vRJ4Wbd9JUmaDbodrrwr8BnghTTDlL8PvKeqVvQwNkmS1J3NaZLbzoR0skcISZI0lLodrnwKcAbw+nb9TW3ZS3sRlCRJ6k6SecCVVfX3/Y5FkqTZoNskd0FVndKxfmqS9/YiIEmaS87+yp1dtTvoddv3OBINqqpam+Q1gEmuJEl0n+TemeRNwJfa9UOB/+pNSJIkaYr+PclngX8F7l9XWFU/7l9IkiT1R7dJ7v8APkvzKXEB/07z7DxJktR/L2iXH+koK+AP+xCLJEl91W2S+9fAW6vqlwBJHg/8HU3yK0mS+qiq9ut3DJIkzRabdNnuWesSXICqugv4nd6EJEmSpiLJNkk+mWRZ+/WJJNv0Oy5Jkvqh2yR3kyTbrVtpr+R2exVYkiT11snAfcAb2q97aZ6CIEnSnNNtovoJmkktvkJzj88bgON6FpUkSZqKJ1fVwR3rf5Xkir5FI0lSH3V1JbeqTgMOBm4HVgEHVdU/9zIwSZLUtV8nedG6lSQvBH7dx3gkSeqbroccV9U1wDU9jEWSJG2YI4DTOu7D/SXw1j7GI0lS33hfrSRJAyrJe6rq08BWVfXsJFsDVNW9fQ5NkqS+6XbiKUmSNPuse2b9Z6BJbk1wJUlznVdyx3DHicd31W6HI47ucSSSpF6444Tzu267w5EH9DCSjXZtkhuBBUmu7CgPUFX1rP6EJUlS/5jkSpI0oKrq0CQ7ARcCr+l3PJIkzQYmuZIkDbCqug149rr19rn2u1XVleP3kiRpeHlPriRJAy7JxUm2TvJ44CfAKUk+2e+4JEnqB6/kSpLGdeOnbuu67R7v3amHkWgS21TVvUneDpxSVR8adY+uJElzhldyJUkafJsmeQLwBuDr/Q5GkqR+MsmVJGnwfYRm8qnlVXVZkicBP+tzTJIk9YXDlSVJGnBV9WXgyx3rNwAH9y8iSZL6xyR3DrnwC6/suu3L37a0h5FIkqZTkgXAO4A96Di3V9X/6FdMkiT1i0muJEmD71zg34BvA2v7HIta73//+7ntttvYaYBcHmwAACAASURBVKed+NjHPtbvcNYz2+OTBp1/Y/1jkitJ0uDboqo+0O8gNNJtt93GLbfc0u8wxjXb45MGnX9j/ePEU5IkDb6vJ+n+nhRJkoaYSa4kSYPvPTSJ7q+T3JvkviT39jsoSZL6weHKkiQNuKqa3+8YJElzwyDca2ySK0nSEEiyHbAX8Lh1ZVV1Sf8imlsuPenV65U9cM8D7XLliPrnL/n6jMXV6ZQvvmzE+r33rm2Xt6xXd/hbL5qxuKQNMdsSre+evmq9sl/ft/aRZWf9foctmLG4emEQ7jU2yZUkacAleTvNkOVdgSuA5wGXAn/Yz7gkqVcGIdFS/5jkTpOVJ7yv67Y7H/nJHkYiSZqD3gP8HvDDqtovydOAv+pzTJLmmNl2dVVzl0muJEmD74GqeiAJSR5bVdcleWq/g5LUfxuSeG5osrohV1dNjNULJrmSJA2+FUm2Bb4GfCvJL4GVfY5Js9xWW41cDguTppE2JPGcyaHA3e7r6HNuHrG+avWaR5addccfuNv0BjjH3f6py9crW3v3g48sO+t3fO9zZiyuyZjkSpI04KrqwPblh5N8F9gGuGCyfkkWAZ8G5gGfr6qPjqr/M+CwdnVT4OnAgqq6K8mNwH3AWmBNVS2cjmMZJttumRHL2Wb/l87rdwg94b2ami222WrBiKVmjkmuJEkDLMkmwJVV9UyAqvpel/3mAScALwVWAJclOa+qrlnXpqo+Dny8bX8A8D+r6q6OzexXVXdOz5EMn8Nf8th+hyD11IFf/f6I9dWrmxnFb139wIi6cw5+0YzGNVsctujP+x3CnNWzJDfJycCrgTvWnXiTPB74V2AP4EbgDVX1y7buWOBtNJ8IH11VF7blzwFOBTYHlgLvqarqVdySJA2Sqno4yU+S7F5VN02h677A8qq6ASDJmcBi4Jpx2h8KfGnjopXUSwd85ez1yn69ejUAK1evHlF//usOeuT14q+MHPhx/+pftX1+tV7dua9btFExvv6rV45Yv3v1QwDcuvqh9eq+fPCzNmpfc4nD9Efq5ZXcU4HPAqd1lB0DfKeqPprkmHb9A0n2Bg4BngHsDHw7yVOqai3wOWAJ8EOaJHcR8M0exi3NOa/+yuldt/366w6bvJE0g24//uKu2+549Et6FkefPQG4OsmPgPvXFVbVayboswvQeZPbCuC5YzVMsgXN+feojuICLkpSwD9W1Unj9F1Ccx5n9913n/xIJKmHhjUZdJj+SD1LcqvqkiR7jCpeDLykff1F4GLgA235mVX1IPDzJMuBfdv7fbauqksBkpwGvBaTXEmSOm1FM3pqnQD/e5I+Y90oOt5IqQOAH4waqvzCqlqZZAeaya6uq6pL1ttgk/yeBLBw4UJHYs1RvUwsXnHukSPWH7r/DgBuuf+O9eq+ufiEad23+ucxW28/Ytktk8GNt2Dz7UYsZ6OZvid3x6q6FaCqbm1PjNB8mvzDjnYr2rLftK9Hl0uSpEdtOvpe3CSbT9JnBdA5DemujD8j8yGMGqpcVSvb5R1JzqEZ/rxekjubDOsVnEFgYtE/mb/1iGU3Npm/NQ+3y6nta1s2aZe9tsfiP+v5PjS2Y5//jn6HMKnZMvHUeJ8mT+VTZodEac571Vc/33Xbbxz89h5GImkmJHkX8CfAk5J03sw2H/jBJN0vA/ZKsidwC00i+8Yx9rEN8AfAmzrKtgQ2qar72tcvAz6yMccyE4Y50TKB13ged8BEdy2MbfMD3rBB+9ryNW+Zcp9N5m83YilNh5lOcm9P8oT2Ku4TgDva8vE+TV7Rvh5dPiaHREmS5pgzaG7h+X9p5rlY575RQ4vXU1VrkhwFXEjzCKGTq+rqJEe09Se2TQ8ELqqq+zu67wickwSa9xJnVNWkjyxS7wxzAq/htvVr3tXvEAbOjZ+6bb2yNXevfWTZWb/He3easbhmk5lOcs8D3gp8tF2e21F+RpJP0kw8tRfwo6pam+S+JM8D/gN4C/CZGY5ZkqRZqaruAe6hmfl4Q/ovpZnUsbPsxFHrp9JMJtlZdgPw7A3Zp9Rrmf+YZjjg/MdMqZ9Xw6Xh0ctHCH2JZpKp7ZOsAD5Ek9yeleRtwE3A6wHaT47PonlswRrgyHZmZYB38egjhL6Jk05JkiTNah8+6+Uj1u9avaZd3rJe3YffcOG07vsxB27YLWteDR8+J5xz+3pl96xe+8iys/7IA3ecsbjUe72cXXm8T5X3H6f9ccBxY5QvA545jaFJkqQ5YOUJ7xuxvvaeVY8sR9ftfOQnZyyujfWVU9Z/Tunqe3/TLm8ZUf+6wx1FLmnumS0TT81Z152wuKt2Tzvy3MkbzVGjPxGesO00f1osSdo4DhGVJE03k1xJmgZjDYkaj0OipEc5RFT98Mpz/ma9sodWN3O1rVx914j6pQf+xYzFJWl6mORKkiRpVvJKv9Sd39p8+xHLbgzz35dJriRJ0oCbv1WAapezz2PnN/E1y+55pV/TbYutF4xYDos/ff6xU+4zzH9fJrmSJGlO2H6LzUYsh8kB+8/ut3T/7VXz+h3CrDLMV9BmuxctnnoyqMEzu/8jSpIkTZMPvOjJ/Q5Bs9nWjyPtstc25AqaifHc4M95epjkSpKkGbPqc/8yYn3tPfc9shxdt+Bdb5qxuCbim86Zcfg56z8a6fbVv2mXt4yoP+XA6X800maL95n2bU6nYR5aqkf5c54eJrmSJEkT8E2nxuMHIBokt/3d8hHra3/5m0eWo+t2+l+/PWNxTWRD/8ZMciVJkqQN4AcgUm9t6N+YSa4kSZLUI6/66ufXK3tw9b0ArFx974j6bxz89hmLSxpmJrmSpGk3etjTRGbLkChJmk1e/ZXTR6w/sLq5f33l6vvWq/v66w6bsbikQWCSK0mS+mbBFluNWEqdNt26eb5us5SGz39+/o4R6w/eu/aR5ei633n7DjMW13Sb6fvXTXIlSVLf/PmLX97vENZz3QmLR6z/5p772+XKEXVPO/LcGY1rLtph8ex6q/qqs48fsf7g6rsBWLn67vXqvnHQ0TMWlzTbdXNv7R0nnL9e2dr2/+/ae+4fUb/DkQdMuK3Z9Z9DkiRJktRz22/+WyOWw8QkV5IkSZpBmb/liKXUD8fu+95+h9AzJrmSJEnSDNrsNftNuU/mbzViKWl8JrmaNY4/vfv7so4+7MIeRiJJkjS5bL3FiGUvPfaART3fhzQsTHIlSZJmiZmegVQbZ7PXvKDfIUiz0u3HXzxife3dv35kObpux6NfMu52Fmyx9Yhlt0xyJUmSJvD4LTYBHm6XvdXNDKSShtfjt1wwYjnXffBFB21QP5NcSZI0cGbyiue7X7R5T7cvaXabyf83S/b7YE+3P1eY5EqSpIHjFU9JM8X/N4On9+NuJEmSJEmaIV7JlSRJ6pMLv/DKEeu/uvehdrlyRN3L37Z0RuOSpOm0YIttRyx7zSRXkiTNenecePyI9bX33P3IsrNuhyOOntG4JEmTO/YFh83o/kxyJUmSJKl19lfuHLG+evXDjyw76w563fYzGpe65z25kiRJkqShYZIrSZIkSRoaQz1cedXn/qXrtgve9aYeRiJJkiRJmglDneRKkqThtGDLLUYsh8U2WwKkXc5O73//+7ntttvYaaed+NjHPtbvcCRpPSa5kiRp4HzwxS/odwg98YY/3KzfIUzqtttu45Zbbul3GNKM2Xr+ghFLzX4muZIkzVFJFgGfBuYBn6+qj46q/zNg3XMfNgWeDiyoqrsm6ytJw+I1B/x5v0PQFDnxlCRJc1CSecAJwCuAvYFDk+zd2aaqPl5V+1TVPsCxwPfaBHfSvpIk9YtJriRJc9O+wPKquqGqHgLOBBZP0P5Q4Esb2FeSpBljkitJ0ty0C3Bzx/qKtmw9SbYAFgFf3YC+S5IsS7Js1apVGx20JEmT8Z5cSZLmpoxRVuO0PQD4QVXdNdW+VXUScBLAwoULx9u+ZqnjT3/5emV337emXd4yov7owy6csbgkaSJeyZUkaW5aAezWsb4rsHKctofw6FDlqfaVJGlGmeRKkjQ3XQbslWTPJJvRJLLnjW6UZBvgD4Bzp9pXkqR+cLiyJElzUFWtSXIUcCHNY4BOrqqrkxzR1p/YNj0QuKiq7p+s78wegSRJYzPJlSRpjqqqpcDSUWUnjlo/FTi1m76SJM0GJrmSJEnq2hZbBah2KUmzj0muJEmSuvaCRfP6HYIkTciJpyRJkiRJQ8MkV5IkSZI0NExyJUmSJElDwyRXkiRJkjQ0THIlSZIkSUPD2ZUlaY74z8/f0XXb33n7Dj2MRJIkqXe8kitJkiRJGhomuZIkSZKkoWGSK0mSJEkaGia5kiRJkqSh0ZckN8mNSa5KckWSZW3Z45N8K8nP2uV2He2PTbI8yfVJXt6PmCVJkiRJs18/r+TuV1X7VNXCdv0Y4DtVtRfwnXadJHsDhwDPABYB/5BkXj8CliRJkiTNbrNpuPJi4Ivt6y8Cr+0oP7OqHqyqnwPLgX37EJ8kSZIkaZbrV5JbwEVJLk+ypC3bsapuBWiX6x7SuAtwc0ffFW2ZJEmSJEkjbNqn/b6wqlYm2QH4VpLrJmibMcpqzIZNwrwEYPfdd9/4KCVJkiRJA6UvV3KramW7vAM4h2b48e1JngDQLu9om68AduvoviuwcpztnlRVC6tq4YIFC3oVviRJkiRplprxJDfJlknmr3sNvAz4KXAe8Na22VuBc9vX5wGHJHlskj2BvYAfzWzUkiRJkqRB0I/hyjsC5yRZt/8zquqCJJcBZyV5G3AT8HqAqro6yVnANcAa4MiqWtuHuCVJkiRJs9yMJ7lVdQPw7DHK/wvYf5w+xwHH9Tg0SZIkSdKAm02PEJIkSZIkaaOY5EqSJEmShoZJriRJkiRpaJjkSpIkSZKGhkmuJEmSJGlomORKkiRJkoaGSa4kSZIkaWiY5EqSJEmShoZJriRJkiRpaJjkSpIkSZKGhkmuJEmSJGlomORKkjRHJVmU5Poky5McM06blyS5IsnVSb7XUX5jkqvaumUzF7UkSRPbtN8BSJKkmZdkHnAC8FJgBXBZkvOq6pqONtsC/wAsqqqbkuwwajP7VdWdMxa0JEld8EquJElz077A8qq6oaoeAs4EFo9q80bg7Kq6CaCq7pjhGCVJmjKTXEmS5qZdgJs71le0ZZ2eAmyX5OIklyd5S0ddARe15UvG20mSJUmWJVm2atWqaQtekqTxOFxZkqS5KWOU1aj1TYHnAPsDmwOXJvlhVf0f4IVVtbIdwvytJNdV1SXrbbDqJOAkgIULF47eviRJ084ruZIkzU0rgN061ncFVo7R5oKqur+99/YS4NkAVbWyXd4BnEMz/FmSpL4zyZUkaW66DNgryZ5JNgMOAc4b1eZc4PeTbJpkC+C5wLVJtkwyHyDJlsDLgJ/OYOySJI3L4cqSJM1BVbUmyVHAhcA84OSqujrJEW39iVV1bZILgCuBh4HPV9VPkzwJOCcJNO8lzqiqC/pzJJIkjWSSK0nSHFVVS4Glo8pOHLX+ceDjo8puoB22LEnSbONwZUmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0DDJlSRJkiQNDZNcSZIkSdLQMMmVJEmSJA0Nk1xJkiRJ0tAwyZUkSZIkDQ2TXEmSJEnS0BiYJDfJoiTXJ1me5Jh+xyNJ0qDr5tya5CVJrkhydZLvTaWvJEn9MBBJbpJ5wAnAK4C9gUOT7N3fqCRJGlzdnFuTbAv8A/CaqnoG8Ppu+0qS1C8DkeQC+wLLq+qGqnoIOBNY3OeYJEkaZN2cW98InF1VNwFU1R1T6CtJUl8MSpK7C3Bzx/qKtkySJG2Ybs6tTwG2S3JxksuTvGUKfSVJ6otUVb9jmFSS1wMvr6q3t+tvBvatqneParcEWNKuPhW4fozNbQ/cuQFhzGQ/9+W+3Ndw7GtD+7mv2bmvJ1bVgg3Y5qzUzbk1yWeBhcD+wObApcCrgGdP1rdjG7Pt3Oz/APflvub2vja0n/uanfsa+9xcVbP+C3g+cGHH+rHAsRu4rWWzvZ/7cl/uazj2NQgxuq/p6TeIX92cW4FjgA93rH+B5r7caTsvD/vvxmyP0X25r7m2r0GI0X1tfL9BGa58GbBXkj2TbAYcApzX55gkSRpk3ZxbzwV+P8mmSbYAngtc22VfSZL6YtN+B9CNqlqT5CjgQmAecHJVXd3nsCRJGljjnVuTHNHWn1hV1ya5ALgSeBj4fFX9FMDzsiRpthqIJBegqpYCS6dhUycNQD/35b7c13Dsa0P7ua/B2tfAGuvcWlUnjlr/OPDxbvpuhGH+3ZjtMbov9zXX9rWh/dzXAO1rICaekiRJkiSpG4NyT64kSZIkSZMyyZV6LI3d+h2HJElqeG6WhptJ7iwwrP9ok3ynm7Ix2nw1yauSTOn3s/0+7pBk53VfU+nfK9XcE/C1qfZLskmSF8xUv+mU5LEz0acX+0ry+Im+NiTG2WBYj0vqFc/N67Xx3Izn5n7ta1jPYcN6XLPBwEw8NVOSHDRRfVWdPd37rKpK8jXgOd20T/K+Sbb3yWkJbAMleRywBbB9ku2AtFVbA92c3D4HHA4cn+TLwKlVdd0k+/wT4CPAf9HMAApQwN5TPwJI8sSq+sU4dfNong/536ewyR8m+b2quqzbDlX1cJJP0DyPsmsb2m9DJTm5qv5Hx/pWNI8d2X86+8zgvi6n+d0JsDvwy/b1tsBNwJ4TxTgTktxHE+OYqmrrMYo36LiSXDXJvp41QZxT+lvZkOPa0Pgme/NQVXdNVK+Z5bl543luHpPn5o3sM4P78tw8cl+emycx1ElukvdX1ceSfIYxvtFVdfQY3Q5olzsALwD+v3Z9P+BiYNwTaZLvV9WLxvhlSLO7MX+515nKP9r57fKpwO/x6LMJDwAumSC+Df2FO36iYMb4Pr4TeC/NSfPHHeX3AidMtK12e98Gvp1kG+BQ4FtJbgb+CfiXqvrNGN3eBzy9qlZNtv1OSZ4P7AJcUlV3JHkWcAzw+8CYn+BX1dokv0qyTVXd0+Wu9gPemeQXwP08+jsx7j+h1kVJDgbOrqnNEjelfkk+VVXvTXI+Y/+tvGaC7rck+VxVvat94/QNmp/VRDakz4zsq6r2BEhyInBeO4MsSV4BdHtC2BZ4C7AHHf9nx/qfsyH/p6pqftv3I8BtwD/T/E4dxqP/H6bruF7dLo9sl//cLg8DfjVBvyn/rWzIcW1EfLP+DdOwmslz80ael8Fzc+f2PDc/ynPzDO/Lc/N6PDdPYqhnV05yQFWdn+StY9VX1Rcn6Pt14B1VdWu7/gTghKqa8NPkjYj1GuApQNf/aJNcBBxcVfe16/OBL1fVonHaP7F9OeYvXFV9ZJx+DwE/Bc4CVvLop7/A+N/HJO+uqs+MF/9EkvwW8Cbgze0+TwdeBPy3qnrJGO0vBvavqrVT2MfHaf4IrwB+G/g68CfA3wL/WFUPTND3LOB5wLf+f/bOO1yWqkrf73cvKkhQFDMCiiiOCAIGBAxgFjGgqCgimMcEqOio4wg6jpExO4iBHwKiAqKiggkEBBG5ZAmCYA4IgqCApO/3x9p9T58+XaHr9Olz7znrfZ56uru6Vu1d1VX1Ve9agfi9gMqbs/59P42qEek+u+uA1YFbgBtpeWM2qp2kLW0vk/S4in6e2NDeB4E7EU88PmD7qLrlu9pMsi1Jy2xvOTDvDNsPb2F7KnAacB5TTy+GniuzvE79zPajmuYNfN9puySdYnubpnlD7EY6V6q2ocV2de3f0BsL22+us0u6k9o8Y/nU5uk2qc1Ty6c2z7RJbZ6+TGpzBQv6SW45OJcCm9jeZ0TzDXoiWvgLIXSVaHaP2J82Qt96rAfc1Pf5JmJ0qqr93wBI2mbg4PoPSacQLkXDuBewM/AC4sL8VeAo21c39O+Lkv4TWM/2qyRtBDzI9rfrjCR9HdiYEPod+36Hr0o6o8LsUuD4cgP0r95M23Uj3TsAm9u+sYwi/hHY1PYlDdsFMeL4nRbL9frR2/d3B1Ydwa5qhGysdraXlddawexH090HTwfeVV4taScPcR/sYjPptvq4shy/hxKjirsSLndtWNV2retiD9vHlNdKwazhVkkvBr5S+rgL0HQz2XW7Vpe0re2fAChiy1ZvYTfSuVLosl1d+/cI26/pfbB9rKT3jtjfZAQmqc2z1GVIbV5OavM0u9TmCbfVR2rzdFKbK1jQf3Jh+SP5VvE0A/xY0veAw4kf8oXACQ02s3nE3uWR+iHA6ZKOLvbPAb7Uwm6kA872VcABwAGS7kMc1L+Q9Dbbh1TZAV8k9kkv0cLvgSOIUdk6Pt8buekh6Q62/1UzovWnMjW5nvVzQ29E2PbVki5uKaIjX/QkPRPYn3ATuwJYH7gQeEgL27WBjegTYNuVrm9d7NTNXW7Hgc9nAbcr881w98EuNpNuq8cuwLuBo8vnk8q8Nhwi6ZXEsd5/YzfjhloVbmh9NnXuaC8CPl4mA6eUeXV03a6XEzfHdyqfrwFeVrM80PkGoct2deofs7thSjoyQW2eretbavMUqc3TbVObU5urSG2eZf8YgzYvaHflHopA/42Ii3j/I/naE6mMNj2mfDzJ9tF1y/fZjfyIve9CJuLCdz/gYtu1F9pyk7BtXx/PatG/LQmRm3bA2T6z2gokbUGccE8iBHJ/2xfULH+G7YdLOsv25mXeObY3a2jnTNtbNM0b+H4927+tW+8Qm2uYHif12P7PdRewMvL9fiJ5Rr9Q3b9i+XOA7YEf2t5c0nbALrZf1dDHVwB7AusSrltbAT+1vf047VThstW3XbWuW8l0JL0OeB9xbvUush52fKjCDa3HKCP4k0DSWoR2tIp5G/VcmYf+3YW4sXhsmXUSsF+LJ3zJLJmkNnd1fUttnmaX2jxll9q8EpLaPG35Ba/NC/5JbuEuxL///otI42hREdouGRtHfsRu+6H9n4tovbqpIUesxu8oB2gbQSnuL5u1PeAk7UfEx1xIuCi83fYtTX0DbpK0GuVCImlD+kbOhrRzTyLRxGqSNodpmR/v2NDWVyTdDfgZcSKcbPvCBptnDXzev2H5fg4iTr6PEokr9ujr7zButn2VooTAEtsnKGJRmtiTSGBymu3tJG0M7Dduu9kIpaSDgT1tX1M+r03cZFWO1HWxmYe27ga8lRjR7xeA2puYwpuAB9i+smnB2QilIlvqy4f0sW5/dN4uSTv07CT17KpcKXuMeq502q6u/SuCuWfDNiRzwyS1uZPrW2pzanMFqc3z11Zq80zb1OYhLIo/ubb3GNVG0lbAJ4EHA7cHlgL/dHMmRhjDI3bbZ0p6REMfB91s1gMuosLNRhXlDfoOuKryBu8CLgM2K9P/FJumBBzvBo4D7ivpMGAbYPeaTXpK+X5doL8v1wHvqLHD9tbl5HsUMerzPUmr2b5bjc20C5ik2wGbAH+wfUVde8Bqtn8kSUWE9pV0MrHNw7hGkR7/JOAwSVcQMVRN3OiIS+q5hV0k6UHjtlN1evg2yTQ27YkULHcv27yhf11sJt3WYUSM2zOA1wAvBdpmCP0FDdkNe2gWZQAIt8iLiHPnPUSimqYbyE7bVZ6E3ZEQw88DzyPiqJoY9VyBDts1av80O1e0ZAxMWJvH4pae2pzaXEhtnr+2Upun9zO1uYJF8Se348jDp4hYnyOAhxMpxx/QssmR/eoHRG4JsAXNB/d7CVeXaW42Nct3SpRAc8zSUGz/QNKZRB9FjNhVjp454gMOlvRct8zm16Pc+DyGENF1CAE/ucHmAOCTtn+hiBX4KRFAfxdJb7F9eI35jZKWAJdIej3wB6K0RRXPIjIp7k1cFO5EdTKRfn6vSHn/DaJcw9VEEo6x2rljEo3CEklruyQ7UbiYNF1buthMuq272v6CpD3LTdeJktqO7N4KnC3pBKbH/QzLWviMIfPa8gDbO0t6lu2DJX0Z+F6DTdft2tr2ppLOtb2fwtW0zdO0Uc8V6LZdo/bvIy36nswhE9bmTvFuqc2pzRWkNs9fW6nN00ltrmBR/Mml24gKti+VtNSR+v4gRerxRjo+Yu+/kN1CZDxrEpOR3Gxst3GlGcbnbD95VCPFkPLTgPvbfo+k9SQ90vbQERxJu9o+FNhAQ0a2a0azIYLfzyDiC77tdi5bj/GU+9oewC9tP1vhmnUskdikir2Ikak3Ejc02xOjbkOx/c++j62D/W0/p7zdt1yU70TcJMyJXQ8NZJp0vZvd/sCpko4sn3cmYl7q6GIz6bZ6NR//pHC1+SPxJKMN3yhTI55dTFWvj9dI2oSoX7dBS5tRt+uG8nq9pHsTT8Ha3GSPdK4M9HGU7Rqpf/1PixSum+vZvrihjWS8TEybO+oypDanNg+3S22ev7ZSm6eT2lzBYvmT22Xk4XpJtydGfD5EZAdsk/KacuEaVji60q++o8j13GxOZgQ3G0kPBP4PuIftTRRF1p9p+78rTCrdihr4DFGHbHviBuY64uagytWrt3/X6NDWPQiXq8cCeynqB57SsF/7Szw8iXgygO0/S7VhCdj+eXn7D0KEa9Fwl6O/E+L/ZtuXVdj1l784r9d8i/a62o2cadL2lxTlI3rH906uSXrS1WbSbQH/XZ4ivJlwj1yLGO1vxB2yFqqbG+aBijimdwHfIs6ddzU01XW7vl2eQHwYOJM4nj7XZDTquVLosl2d+idpR2Lk+PbA/SQ9DHjPC9hIvgAAIABJREFUKC5RSWcmps1ddLl8n9qc2jzMLrV5ntoitXmQ1OaqdXhxZFc+3fYjJZ1EFBT/M3C6azKIKTLa/YXYuXsTo22fsX1pi/b6yyKsCjwXuMX2W4cs29n3XNLqTBUT77nZHOYoK1DXvxOBfYii6r3Miufb3qRi+cuAt9T0sap+2pm2t9CIGRy7osgU9zjCNepxRPxOZbHpctOzP+GicQKwcRHRVYDzbW9cYzvsd+sJ44xi9YoEIX8Evkz8Xi8E7glcDPy77cdXtPNr4L5ML3vxJ0LkXulSQ2+Mdl0zTW5G3MSYSCxyTt3yXW0m3VZX1CFrYRH7GW6Ytt85l33tgqQ7EPUGG7MkjnquzEP/lhHH/I/7rlPnuj7eKhkDk9TmUXS5LJ/aPCZSm8dil9o8BlKbpy274LV5Seeerlz0Rh7+kxh5uAD4UJ1BcVO4jUgYcRTwtjZ/cIvtsr7pFEfh6UdVLP4R4mJ+OfFI/3Nl+gdwfkM7/yRiXJ5KPP7/SpOIFu7omW5JdaPMdyJiE3YcMtXFLNwsaSksz+B4N2KfDkXShyS9Zsj8vdWQ7VDSr4BPE6Oc/48QxUoRLbwaeD2RYW4v238u859Ac4Hsy4jfqPd7XUvceD2Q4SNUT7X9WdvX2b7W9oHA021/FVi7pp3jynLr2L4r4WL2NeKG8DNzYHdzOYaWu9kBD6tZHkl7EgkT1iHiOQ6V9IZx28xDW+tKOlrSXyX9RdJRktq6RB1EPJG5hUi28CXCNbOWco1ZavtW2wcV27o+3knSRyWdUaaPaKoW3Vi3S9LtJL1R4Vp2GPASRUKYJkY9V7puV9f+3dJGcJM5YWLaPKIuQ2rzclKbh5LaPH9tpTZPt0ttrsL2gp+AOwyZd5cGm8cDvwFOJBJUXA48tmV7d+mb1iHijS5usDmpzbyB759f+ngwcaJeDjyvRf+OBTYEziyfnwccW7P8mR33+4uJG5ffE3EWFwM71yx/AbBkyPwlxOhtXVtLJ3xMVf5ewC+GfPfT8nstKdPziRICAGfXtHNG1bw5svsh4X7ySSLu6ePAqQ374lxg9b7PqwPnjttmHtr6AeHCs0qZdgd+0PL4WFZez+ubd3LTMUU8nfoScaO/N3BOg81RRPmJ+5fp3cDX52K7iKyIBxMjq9sTNwufb2E30rkyi+3q2r8vEMXszyVqtn4SOKDN75zT7CYmqM100OVil9qc2jysndTm+WsrtXm6XWpzxbRYYnK/LunZtm8GUCQv+A6wZY3N/sCTXYKdFbEyhzfY9FgGy4vH30II3MsbbO4m6f4u8R+S7kdzvM07idp/VxSbuxEXwiNrreB1wIHAxpL+UPq3a83y9UEwFdg+rLgbPKGs49mur49n2zNGk23fJjUE4sA9JH0c2LZ8PgnY23Zl1kJJn6TeHW1Ytr0ed1Nf3UNJ6xE3TjA9nqjHiwlR+kxp8zRgV0VQ/etr2vmbpLcRNRABXgBcXUbhK0feZ2HXJdOkiIyFPW6l+ZjpYjPptu7mGLHt8f8k7dXCDrplLXwJcZP1emL/35dwqaxjQ9v9y+wn6ewGm67b9QhPd2c8XuFC18So5wp0266u/XsDcS39F3GN/x6RhCOZeyapzV10GVKbi0lq8wCpzfPXVmrzdFKbK1gsf3K/ARwh6bnEwfktauJYCrdzXzYv279s+Xgd213S+u8N/FgRYwORrayp4PwST68ZdxUtXNCLWD9RETe0xPZ1DSYvGZwhaR3gKpfhliHfLyFG5DYhsme24XpJG9m+ZGBdGzGVna2Kg4gbiN4NwUvKvKfU2JzR934/6muDDfJm4CfFFUtEprjXln06I7FB2ec7VqzrJzXtvKj06xulnZ+UeUuJEeex2rlbpsmDgJ9J6pXmeDYxAjdum0m3daWkXZnK5LkL7etqDmYt3I6GrIWeyuR4I3E8tuEGSdva/gmApG1oPle6btetkja0/avS1v2ZfoNSxUjnSqHLdnXqn+3rCSFd4eKrFgET0+aOugypzZDaPIzU5vlrK7V5OqnNFSyKxFMAkl5HxMdsALzadm3JAUlfJEb1er76LwZWcYvi9ZJ2GjL774R7RGUhc0VAdi+hwkW2/1W1bFn+w8CmTJ0QLyhtVCXS2NX2oaooPO+KMgCKzHIfAP5GXBQOIUZ7lgC72R6a/l5RZP7trk9x37/80wh3hP8mRt0hgvzfTsTlfLfG9mzbD2uaV2O/PAFHW/p+LxG/V2WQvrrVg5w45dj9IDGyqTLZ9VkEkbQFMVIvwt3lrBZtjWwzybbKqOangEcT14JTiXqStWUFyoj8B2zv09TGgN02wL5E1szlA5CuT4jxMEKMejExVwO7uyZ5xyy26wnETcllxH5cH3iZ7eNbbFvrc2UW2zVS/yR9zPZeqkgw5MyuPBEmpc1ddbnYpjanNs8rqc3TbFKbp9ulNle1vZD/5A4IhogRxPOAs6C+tlv54V9H38lHZHCsFbdi+x3iID2hzHo84QLzQCL99dAgd0lbE0LffxJ9qaGtnZh+gTi6ZtlX2T5Q0tBRUVek9FdklnsHcVAfCDzN9mmSNgYOrxIgSccTJQlOB5aPQtYdoIq6W/sAvWyS5wMfsX1elU1fWwcCXy2znk/cMNWWh+izP9P2Fm2WHejrYIa+ob+XpCOIUfMX0VcP0nZt3UaFm9tbmSnAtds1C7tLgR1d77rWW/Yudd876lLO2mbSbdUhafWBEfWq5Y4HnlD1NKXC5iLiqdEy+kY53SJhjaS1yrLXtm1vwL7tdt0BeBBTgth4PSx2rc+VAbuRtmuU/knawvaZkh437Hv31epLxst8aHNXXS62qc2pzYN2qc0TbquO1ObU5mEsdHflNQc+H10xfwblB/jfMgHLR3NOadHubcCDbf+l2N2DyOb2KEKQZ4ippEOIhBNnM3USmQh0r+vn14Gvl3UslfRi24dVLL5heb3A9hEttqPHKra/X9p4j+3TStsXqT4cp61bx3Jsn09zMephvIyIqfk0U3E1beKtOlFuRh5PXBy+S2RI/AnVv1eXepAQmei+SmTKfA2xb/46h3Z/aSOihf4YN5gacVN5P2yUs4vNpNtC0n2AexFufTdJujvh5rQ7kSW0ibOAb5YbqP6byKElPQp/t31si3X3+rgUWNv2lbavlXR7Sa8E3mT7wRU2s9qucl08t6zrSZLeavtJDf0c6Vzpsl0d+/dhIibx6bbfVrfeZOzMhzaPrMtludRmUpuHkNo8+bZSmytIba5uOKfp2byWEn7wbwE2KfOeQbgNnNVyHecNfBYlA2HVOoii3mq5/rUIN6FPAU8u6389kc3xm3X9Am7HiBkZ+5cftK1bF/DBNvNm+Xu9fha21xEp068lEpH03l8HXNv0GxMuYeeUz/cAjqlZ/vTyehIxEr4OcFmLPvYyAZ7bN+/EcdsBO5Xp44QA79I3b6dx/mYr+kQIy1+JrJtnEjchVwEfBe7Vch0HDZm+WLHsFmX6AHFxf3TfvC0qbF5IuFr+kcgyux2RKfXoGptO20VkQ/wlUWrgUEIQzyBuVBqPjVHOlY7b1al/RMbYxxHX3s3793lVWznN38QstZkOuly+S23u9nulNo/BjtTm/n2R2jzdLrW5YVroT3IBUGRffAsz3Y2GuYd8gUiAcTrwCUm/IQ7s/7D9jZZNnizp20ThaIgsbCcpgrmvqbA5nyhA/qcW6z+E8IP/KfAKwoXo9sCzbNdlOTsOuBJYXdK1TI2aNcV2bNa3/GrlPeXzqhU2AE8CBkdhnjZk3mx4GXFDMTK2G58a1HCDI7PkLcVt4wpqRh+Zqgf5LiK5yhrAf7Vo5+by+idJOxAXmDb14Ea160+8cT1xg9bDlCcSg0haBbjVtiXdl3gqcmndcdjFZsJtvQp4kO2/KWJkLiVKlJxW179+3CJ2v4/9Bz4/vH9VhFAM8p/AlrYvVcQ0/RR4oWtcIum+XfsX258S5+9pwLtsf7zBrsco50qX7erav/8C/oM4LwbdY6v2ezJGJqzNXXQZUpu7kto8HrvU5ilSm2f2L7W5jlH+Ea+sE3AO8O/AI4kyA1uWH2vYsudT6sERIvEP4J4jtieivt1HgY+V97UjwUSc0NWEm8y3elPFsv31vZYWuzVH6F/liPKY9ve/EyNE/yTcE3rT5cChY26rU53AMbT7GeDOhLvRJYQLzEFz0M4ziHirTcoxsgx45lzZjdi3VxIJT35b3v+SKItwMfC2cdnMQ1uDT0Rq60BWrGNVIm7wM8AXe9MY9/1gHy/qYNNqu4bY/WrEvrY+V8a0XaP2713j+l1yGm1igtpMB10udqnN3dpKbR6j3Yh9S22uXkdq89TyC16bF3TiqR6SltluU992RpKDwc9zxSgB1uPoo6T1gY1s/1BRD24VN5craLvuOwFrA+8nRmN6XOcWCQUkHUxklLumfF4b2N9Dsh1KuoUY3ZzxFS0yD44DSRsAa9k+t2aZOxBPDjZg+hOLpjp3E0UjZJqU9AsiscqahFvJ+ravlHRH4Oe2HzIOm3lo6wqmahhCuOks/+z6Go29dXRNZrIDM/f9jGNE0u+ZPsL5pv7PHpK4p+t2KUqn9Jd1+Uj/Z9fHMg2uawNqzpWO2zWr/ilKqrwIuL/t95SR9HvaPr1pe5LZkdo8tL3U5jGR2pzaPGQdqc3D17UBC1CbF4W7MnCMpNcSvuPLM3pVXNQ3ltT7kQVsWD73LsybNjWmSOv/SeDBhKvSUuCfdRf1YYJZQ89FqdfHnptS25TyryRcCO5CJLxYFziACPQeBzcDf7C9S2nvQcDTibikNifdpj0RBbB9taSqEgLnecTyAuNCkShgfcp5JOmxtk+qWPybRDzDMvqOwZp117lL2fbQgthd7fo4hBCAp9AnABXL3mT7aqKQ/aW2ryyNXC+pqpB4F5tJtzVYXmDZ0KXqGTmZiaQDiPp92wGfJ540VV3MP8f0JD2Dn4fRdbtOZLrLXP/nSne5fkY4V7ps12z792kiKdH2xDF/HXAUkX02mVsmps1ddLn0JbV5itTm1Ob5bCu1eTqpzQ0slj+5Ly2v/QeSGe57XpshrCWfIkZijiB8+HcDHlBnMIoA2146y/69jnAP+1lZ3yWKTG7j4jhixPESSQ8g/PEPA54h6ZG2/6PWGpZIWrtcBHvp5leoY1XSB4nahxcwPeNmlZCua/upIzQxLGX86sR+vStRE3Gcdj1GEYDVyg3OEuD25X2vfl9VPFgXm4m2Zfvgmn60pRd3dY0iRf+fiScFdWxte1NJ59reT9L+VIiAK0qK1NF1uzxaDNMMRjlXOm7XrPoHPMr2FpJ65WuulnT7Wa4zaccktXlkXYbU5gFSm1Ob562t1OYZdqnNDaxQF6e5wvb9Rlj2N2Nq81JJS23fChwkqbbAPcMFeKNx9GUI/3KkKAeWJwAYp9/62rYvKe9fStTre0M5OJcx3U1qGPsDp0o6snzeGXhfxbKjlFsYJ88mEgW0qkVGbM9D3VBTsIft5QkPJK0J7AnsQbiwDCZDmLVdH6MIwJ+Yclf5M9NdWf48RptJtzUOhiUzeVeDzQ3l9XpJ9yayK7a+dq3AjHquTJqbFaURInAz6ljeNr9dWhxMWps76DKkNveT2pzaPJ9tjYPU5ikWvDYvij+5AOpY8Lgj1xfROFvSh4gTevUmo44C3IUTJb2DGE17EvBa4Jgxrr9flLcnUq9TxLvxALX9JUWR++2Jkb2dbF9Qsez/jKG/XbiMKPnQ9uKwLbC7pMuLTaOLXRklfxPhlnQwkTr96qaGutoVWmeatL1dy3XOymbSbY0D258vb0+kPrNnP9+WdGfifDmTOI8+X2+yUjDquTJpPkG4y95d0vsIV7Smm55kTExQmzvpculPajOpzT1Sm+enrXGQ2jyNBa/NiyXx1LsZUvDY9vPmqL31gb8Qrk17E5n0PmP70hqbk4AnEifOnwkB3t32ZnPQvyWEi0yvjt/3bH9ujOs/lNiGPxAjw/crsRZ3JurBDd0mSWs5CkzfZdj3bpEYY1JIOgrYDPgR02PJqhIErD9sftXTCUkfJurgHQh82vY/Wvark10yXiT9ikiXfzJwUtWNYI39HYBVbf99Lvo3pL29bH+sqU+DI77D5g2xG+lcKTa9PxStKNe0rWx3+vMhaWMi7lHAj2xXxbolY2SS2txFl4tdanNqc//yqc0rManN05ZZ8Nq8WP7knkf8kGfZ3kzSPYDP296xwXRilAvtFcSoyigCPHImRklb2l42MG9H22MZMS792BO4F5Ga/Zwyf2tgQ9uHVNh92/Yzyohq/4HZG1kdOupWTqLn2f7aOPrfBkkvHTa/KbZCEV/V/8TitxXL3UZcdG5h+L4YmsCkq12f/UqRaXISKGp4/h9wD9ubSNqUKPXw3y1s70DU/XsMsA2wMVFw/TkNdlszc9/PeKolaV1gA9s/KZ/fRIzsA3y56boxZH2/tb1ewzIzMsUOmzfEbuRzpVwDjiTKGbS6CZH0U9uPbrPsgN0htl/SNC8ZP6nNM2xSm2dJavPCJ7V5xjKpzVXrWCR/ck+3/UhJy4jsaNcRdaiGpikvNhsRafYH3aga3RskbQPsS1/Gsra2o6C+TIy2Nyx9PsB2bSZGSWcCL3WJQZG0C7CX7UeNs3+TRNJJth873/2oQtIzibibexM3TOsTaesrj8H5QNJxTGWaXD5a5754osWCpBOJhDifdckSKul825u0sF2FyAD4OMId7q7AubZfXWNzCJFR9Wz6kkAMG1WVdDhwmO1vl88XE08I7ghsbPvFrTc07H9n+74V390TuA9wKJHOX+WrtYjrzcajtNWyP2sScZB7EMlJvgh8xfa1NTb7ETU/v+4RhG3wZkARA3Se7X/r2v+kHZPU5knpcmkrtbmQ2jweUpunSG1e/l1qcwOLJSb3jOKO8zniAvEPqtN/9zgIeDdROH474gdVrcUUXyBGfKddjIZRRrIrf3TXl0XomonxecCRkl5MnOS7Ee5RKwwaSGsO4OoSAAA/kPQW4Kv0ZTH0HLlRdbjRei+wFfBD25tL2g7YZS76NktGzTQJdPq9OtlMuK072j5dmnba39LUv8K1wHlEQo3P2b6qhc3DgX9rKQIP6olo4frezY6kk1v2sZ+6Np8C7E6UM9mfqevgtcA7qowkfc3286uucXXXtvLE63PA5yQ9Fjgc+Kgi4c17K0bD30TEWN4i6Uaan6y8vfS/v8wLwE3ETUky90xSm1vrMqQ2V5HaPG+kNk+R2hykNjewKP7k2n5teXtAGQ2rLQ5eWM32jyTJEZuxbzlA392iyb/bPrZl957RcrlhdMrEaPsySS8EvgH8Dniy7RsazCaGRi8BANAriP66vnlVpSjGwag3WjfbvkrSEklLbJ9QtnNFY6RMk9Dt9+r4G0+0LeBKSRsyldnveUQ8Xht2IW5SXwu8QpGo5iTbP6qxOR+4Z8s2Bsss9D8huuswA0nXMfz6IGC1qoYc5SoOAXaxfViLvvXYs7yOfI0rI7Y7EOfVBoSAH0a4mH0XeOCQfjbV7Btc/v3A+yW93/bbR+1jMnsmrM2j6DKkNs8gtXleSW2eIrWZ1OZW/R3hyfFKzaijRZJOIX60I4HjiUQNH7D9oBZtfYCopfd1pgdzn9m1/xXtfAi4hhjtfQNx0l5g+50Vyw+O2tydcH/5V+lf3cj0xCjuHZt6xU1rjqRltreUdJ7th5Z5J9t+TMXyPyTStb8fWIdwi3qE7a0n1ukWSLqAqB05SqbJkX+vrr/xhNu6PzFquDVwNbFPdrX96xHWsTGRTGcv4O62ZwiWpGOI83JN4GHEk6z+68Yzh9j8DHiJ7V8Oae9Lth/Zto9tmaTboaTLgBOAL3ggYYWkT1S4iQ3tW9V1XtLGti+SNDRuadzX62Q4k9LmSelyaSu1eZ5IbZ5mk9pcvY7U5m5trVTavCie5HYcLdqL8KF/I+HOsh0hWG3oxc88vG+eibT7g33rH8FR37JtkhH8B5GJ8Tzg1cQoSl1a89mMTI+MIjnAPsy8gZmxHwYYOa25pDsSLhHr2X5VcVkadBsZJzcqkmpcIun1xI1WnTvas4AbCXe5FxPJS2oTRkh6NiFq59muKvo+bp7WwaZLGvquqesn1pbty4AnSlodWOKGpDH9KLIWPgy4lMjiuBvFdXEIHxmlX4V3E2UN3keUNADYknDx2XOYgaTtbR9f3t/P9uV93+1ke2hx+z5GcjuU9BPb29aMUl8FfNj2Z4Z8t6krso8OE9HCPn3vVyXcRZcx5LpbeBMRNzkspm3o9ToZLxPW5ta6XPqW2jyT1GZSm+e7rdTmGaQ2V7AonuR2HGHa2fYRTfNWViRtRoyGA5zskmVxzG2cAxzAzEQJyyqNWH4RGjWt+VdLO7s5su2tBvzU9sNmtRHV7T0CuBC4M3GjtRZxUTitYvnXE8kIWtXEk/QZ4CHAqYS7yzG23ztC/3YCPkiIu2h3Y9Zv35hpUtIniQvOfWj5e3WxmYe23jRsfp/d/9Z9X9bxCOBMj5Zq/4O239Y0r++7TYC3EscJwC+AD9k+v2L55UkcNDOhQ5tMjJcPmW13TNwj6a7AqcOewEk6GNjT9jXl89rA/rZfNrhszfrvS+yPFTG+LiG1eRipzbMjtTm1uY7U5mYWkjYviie5dBstejswKJrD5g1F0g7EAd5/MWoaHdyWKDlwkKR1gDX7R3T6lptNQgwk7Qm8knDbAjhU0oG2P1ln14FbbP9fB7tvlWkUNrT9AkU2SmzfIKkuDqczipiE59veh0iUskcLs3sCP1dkz/wiUf+wboTpscBmtm8tI+EnE4Ldlg8BO3rUmmIVmSaZulj3c0Z5XcbM36tq27rYTLqtkeJHhmH755K2lrQBDSUH+ngSMCiaTxsyr7eu82nvXQJMi0sbPDcazxXb9xuhrZmND7k5k/T4isU37YloWfZqSZuP2OTvgcZsm6VvrcpDJGNnotrcRZeLXWpzkNqc2jyfbaU2D28vtbmCBf0nt2+06HrgbEltRpieBjwduI+kT/R9tRYts7dJOoBwp9qOcFF6Hg0ZIyW9m3CjehCROOH2RFrwbYYs3nNt6iVy6NW2ezGxrU28HHiU7X+Wtj8I/BQYi5BqqmD8MZJeCxzN9P1em1XREUx/e6YC2C+2fXNDszeVEeJeIoINGd3dphVF3LaUpAYx7Lf5T0nvIjJl7gF8StLXiLiGXw0xuak30mj7+g43BX8ZVUQLrTNNutRSk7Sn7Y/3f1du1sZiMw9t7Vf1XVtUUXIAGFZX79+JmL37S+pPurMm8bRg2PprbzQ9JFaI6TcPg8dt43Es6XbAvxM3eQA/Jko41J6bdTdntqsSeSyRtHbv6Uq5ptTqVd/1HqK0wcOAxqdgo/xWyXiYD23uosvFLrV56vvU5tTm+WwrtXl4m6nNVetoeR1YKVFFoeMeHlLwWOEq9DAiJuO/+r66DjihjUuLpHNtb9r3ugZRH6qyFICks4HNCTeKzfvXU2Nziu1tmuYNsTuPSKxwY/m8KvBzlyQNs0VTBeOHXfwbXSjKCNLBwK/LOu5L1A6sS0byZOCdRNmA7xM3ILvb/vHoW9CMpP2BjYinB/0xELWxE+X42gN4KhG8vxXwA9tvHVjuBuASpvbhhkQMSWOyiWL/cWKE+htMv4lp6t8Zth+ucGfb3PZtKrUsa2yGFSI/q3ccj8tmUm0N3EDPYNgN+JB1XEjLkgOS7gSsTSQ++Y++r66ruumU9Fci++rhRDzRtHPN9olDbK4hYh1FuEP2zicB29peu6GfnyeeuvWumy8BbrX9iga7c4gYmmk3Z7ZfVWOzG/F07sgya2fgfbYPqbHpv97fAvza9il1fSt2rX+rZDzMhzZ30eVil9o8Zf94UptTm+eprdTmyn6mNlewoJ/kVgjl2sB9XVGmwBH/co6kewzalxGmjw+zG6CX8v96Sfcmgrib3Alusm1JvdHO1Vu0s7qkbW3/pNhsTdSiauIg4GeSjiZOomcRNQTHgmfpOkGMLD3Z9sUAiiQZhxPB+1Vtfl/SMkKYRMQMXDnLftRxF+J37Q+AN1NuZtOQ9EbgpcCVxFOEfWzfrJIgg4jf6GdjWozg1bAW8eSg/wausn99XFNu/k4CDpN0BRVPSRTuZy8C7jcwerkmsW/GYjPptggXqtnSuuSA7b8TmVR3AdCU69AaktbwkJirsu4nFZsXAd8BDrf9i5qmntX3fjChRpsEG4+wvVnf5+OLSDYxcokO218q5/N2xPm8k+0LGmwOVjxl6p07F7foG4xWHiIZA/OkzV10GVKb+0ltTm2et7ZIba4itbmCBf0nt4ekHwPPJLb3bOCvkk60XRfE/kIidqKf3Wn3J/fbigL3Hyayq5konlzH1yR9FrizpFcSteWabF4OfLGMNkGULGgM/rb9v2WfbFtm7WH7rCa7UZH0OiKhQ3+A+i4enrGtn9v1RLT095cKd4y6tn5EBL9/p2/egXUjUrPBdptYn37WIS4GvxlYz22ShmXWPJ9qIf2XpF8B73RFbbcO/esxSqbJU4mLzzpMz4J3HVBV67KLzUTbGnIDvbqL+2ATml5y4AJJjSUH+mx3JArUN8ZcOdzljgOOk3QHQlB/LOk9rojfGzaCPCK3StrQxYVPUcahTfKO1jdnA1xElIdYpbS3XsVNBeX7pwOfBX5FiO/9JL3aFbVRZ/NbJeNhwtrcRZchtbmf1ObU5nlrK7W5ktTmqrZn8RR4pUHF/UHSK4iR4nerwt2ob4RpWyKhQI81icf/Txyx7TsAq5YRoaZln0SM7olIfvCDlm2sRfyWjW302WxG+O+bucvgeLYHMiiqndvLF0u/+uOZVqkTB0Xtrt8Bx7vEbahFVrpRkfRW2x/S9BiD5TS5y6hFZsQWfVhKBO0fZnto8L7Cze3lzEyy0ioDXjmm+gP9a2O1+uy28ATqi5b+bQRc5pZZMTu08WjiKcoattf7KC3uAAAgAElEQVQr58yrbb+2xuZxdeusEzON6DpUri07ECK6AZHA44u2/1Cx/EZEGYOrCcH+HOEa9Svg5bbPGGbXZ/8E4knTZcQ1an3iJvyEBrvViZszMXVzdpjtuqcDbyBKMfyFEOs2NSEvAp5h+9LyeUPgO7Y3rlh+b+IYPwuYEbs0hhuPpIH50uZRdLksn9pManOLPqQ2pzanNs+0mTdtXhRPcoFVJN0LeD4RG1JH19Gs5ZQT4qvAV8vISqskC0U4f6DI3ljnstHf1vJskSr5D9ycxbmXwfEo4gCdqwyOS6SpBBBFAG7fwu7ficQdbyz9OwloGmG+hkjn/4kyCrRr517X00sYUXvRGWSUkcAmykjhOUXMqziEGG17CjHa+2Km+l7Xz1eX5W8AbqNcwIAZsVoaXqj7W2VbNUxQJb3M9hfL+3WJGJItSt9290AB9T67Q4G9bF8p6SmEW9nFwEaS3uIh5UMk/Y1wAfsyEbM36ojex4j99y0Id0lVFDXv0bv4FvG4oTwNeCDhpjN01LKP1q5DijT+m5R17ueK0gQDHEQkbFiLiBXaC3gOIaafZqqOaNW2/aiI8YOI4+Iityj9MjDSPsNNtYI9iVqara6DhSt6Ilq4jDjXqrgPsDVxc3EOce0/hShv0urGMZk1E9PmrroMqc19pDbXkNqc2pzaPJT502bbC34iAqPPBT5TPt8fOGoO21ufiONYBvwceAtRCH3YslsRmdC+TiS3OB/4czkAntrQzgHEifE7YmTlPCIjYFP/zgVW7/u8OnDuHOyHjxDJH55AjIJ9jXBbqrNZChzaoa2z+t7vXvbF7+f72Ovr0znAXXv9JOIZDpzD9nrtnFteb0eMpDfZXQKs07KN28rF54S+6YbyOrQtInlL7/3XgFcT2faeA/yopq3z+t6fCmxQ3q8DnFNhczHweuLi+AfCnXGrEfbhz4YcW0PbGmK7jMjkep9yfh5NjJDW2fwQWIPIpHp46e+pNfv+ujJd2zddB1xbYXN23/tLq76r6d+qRJH2rxM34XsRT8Oqlv9JeR3sY2+6HHhthe0JxBOiUY75/wO+W87/lwLfJv4M7US4I1bZ3Z4Q1LeU7fojcMEobefUbWKC2swIulyWT22ebpPaPJ72UptTmwdtUpuH281amxfFk1zHSNIRfZ8vA55bZyNpK+KAfjCxo5cC/3SLgt2O2I4PAR8qoyvvIop/Lx2y+KeI0Yo7AccDT7N9mqSNiZPpuJqmtvZUtsj9FFkFm5IXQIz09Pvr91wOxs1bgVcRo78iMit+vs7AUQLgbpJub/umEdo6oG8d/0+RpfJ1Nct3QlOxAkNxdazAyAH+s6Tn4nGNojD5nwm3mSZ+RbtSFxBPX94AfNj2dwEkXW57u5b2D7T9/PL+aEn/VbPsEklr2b6WEJHfAjhGj6uuY/+0/SmiJMR6RCzfZxRxeV+x/Y6G/v1OkTDGiqQJb6TFiHtBjvISLwc+6XCjO7vB5lnEjUhjzJXtJS370c9tfe+vrfmuii8Roth7SrEL8VRi52EL2962vA6tbahScJ7hT4IuI+KYvsP0WJz/renfqoQLVc8t7a9EEpodqU/sshoxgn6nMv2RuBFP5phJavOIugypzdNIbR4bqc2pzYOkNg9n1tq8KP7kqlsMxKeIE+8IokbebsADRmhzA+JC8wJCqAYz9PVYxfb3i817bJ9W+naRmsuvdc0WeRBTGRwBns0YMzjCcveng23vSp/IteTXwCmKrHv9JQAqTyLbny3t9uJq/grsO2K7behlutuJyPp2aPm8C9HvKnoB/iczWoB/Vw5UJBN5F+HSs0Z538TbgVMl/YyGupW2j5R0HPBeSXsAb6Y56+S6ijIAAu4m6XaequVWl8BkP+AESZ8mRn+PkPRN4ilE1c3m8hPIEV/Vu8F9EHFuN/EaYsT2PkTx8u/T/uZMirihFxPXHqi+me6dL990xBXeRnvXoVHYWFHrT8CGmqr7J4a4vA3hQZ6ewfEEtcvgGI2MVnD+t2W6Pe3cKPGICV0kHUhownWEi9ipwP96juLIkplMWptH0GVIbR7Gr0ltni2pzanNg6Q2T+/P2LR5UfzJpWMMhO1LJS11xFkcJGlo8edBykXodoQI71xGp6voH6W5YeC7povSsGyRtaOxMCODo5iDDI6zGPWFGK35I+Eq0xtpqt0XmhlXsx7xGw9N/tAVT8V1vNd2fwzIMZIqawUSGURvJOIZdiVGp/YbZ98GOKgctyfS7iLZ47PEU4vzaDGCaPsfwN6SHkZc/NdoMNmn7/0ZZfmrJd2TEl9T0c7XJJ1JxKs9kLh2PZpIzf+9CrOhSRcc2UHb7HvZfnGL5YaxF3FTcrTtXyiyHVYmgSjny/WS7uQRktSMyINnaX+WpK16N/uSHkXc1NSiDgXnPZWgZpTsmaP+YVoPuAPhBvgH4mbpmjZtJWNjYto8oi5DavMwUptnT2pzavMgqc3TGZs2L7bsyr0i8LcjMiRuX2NzEvBEQpj+TCS82H1gtKTKdmPbF7Xs263EiKiIR/M9dxQRPvW16fn71tMqW6Si9tu5rsj8N04UZRe2IC6QrUZ9i93OHkhWMGzewPcjF7WeDYoi1Tv0bpQk3Q/4ru0HDyx3HTNvAnqjmDcSLkiV5QZm0b/LiWLdB7mhhtmA3am2t+7YpoA1i9vSSo+kS4jYlK8ScYIjX2RHFIKvEXGAP2D6+dJY4H4SlGP+QRR3NKZuVm+jJrtil3NT3bJnHkH8YXoRfX+YbO9ZYyNCeLcu0ybA34gEF++uskvGwyS1eRRdLsunNs+0S22eff9Sm2dJavN0UpurWSxPcrvEQLyEGK18PeGHf18aYoV6FHem5ZkV++bP8OG3Xekm0YSknYbM+zuRCGBo5jJHRrlz1FDXakwMG/Vtw9vpi9OqmdfPpONq9ibiEnpPAzYgEjVMwxUxD7DcDWYT4DDGPKoNbEq4/Xy+3Dx9kYh1aRK5EyS9CjiG6S5RjRntHCNmC0JEAWxvJOmRxH58p6QLiH14aIPpNCEAWgkBUTT+OzXfzzdP7WjX5dwcOXsm8ADbO0t6lqP4/JeBqicJlPUaOF/SNcDfy/QM4JFEwqBkbpmYNo+iy2V+avNMUptnT2rzLEltnkFqcwWL5U/usBiIukD6XpIKiBG9kVxXJB1AZG/bjhhtfh5w+oh9bsPLCbeQnqvF44HTgAcqYogOqbC7F/ALRYHl/lGp1gWW29Bza2iLpKcBTwfuo4gN6bEWzTEyXYtad8L2cYrkJb06X61Stg+so025ga79u46otfa5cgE6HPiopCOB93p6Ovd+XlRe396/OkZzq1ow2D4dOF3S/xAudwczFetVR5cSB3MR6zMWys3Ydzo+Zep0btr+nabHPjYVtx/pD5OkNxIjxNsU21OAnxI3nZl4ajJMTJsnqMuQ2txPavP0dac2j4HU5iC1uZ5F8SfXdi8WpnUMhKRtiOQI6zO98HYb+66ZFUflNuDBtv9S+nwPIlX3o4iDtkpI5zLeZDmSTmBIvE6NK9ofiViQZxJp3ntcR4zO1tE6+90Y2ZI4UVcBNpOE7S+NuhKXxBzjpIxE7wDsQfRxf2JU+jFEKvcHVvSlTXKURYGiqP1ziNHiDYlSA49saz+qEJQbs/cD/8b0J01jvYmRtD1wmu22mTpn+5Spy7nZJXvmqH+YNiDcBvd2RfxRMrdMWJsnpcuQ2txPanMfqc2zJ7V5itTmehbFn1xFTMxzmbroAdVuSoUvED/8MppHKQbpmllxVDboiWjhCiL1+98k3Vxl5JKgAUCluH1xDRg3b+l7vyrxG1SOEtk+hxg9PZpIMX9r6eNSIgh9KJpM9rvBNg8hLq5nM3V8mEjlviJwCfEU4cO2+5OyHFk3atknwBsw/VxpitVq7QY4G5sJt3UO8A3gPbZ/2tSvAboIwUGEG85HiadNe9CXhXKM7A4cIOkqIqPoyUTdvKbMhZ2eMnkq7uk24OByjL2QuLGrYuTsmaP+YbL9pqZlkrllwto8KV2G1Gb6vk9tnk5q8+zbSm2eTmpzBYviTy7wTcKfexl9sQwN/N32sR3b65RZsQMnS/o2U/EwzwVOkrQ6QzKRKeoLfoAI3n4vMZq8DlHnbDfbdXX/Rsb2soFZp0g6cejC0/k+kVjkH+XzamXe0KQLnkz2u0EeDvzbHN2AjINNHdkVZ+D6ZAnHEG6ArTI4Qjc3wK6ug5NsC7j/LH7fLiUOVrP9I0kqLpn7SjqZMceG2t4NoNzoPw/4NJFdsUkPRnVxXIvY5vsQo7c/KJ/3IW5AK4XU9pXEyHKbdna1faikYcJo4nr3rRY3CsnkmaQ2T0qXIbW5105q80xSm2fZFqnNg6Q2V/Vhxb0OjA9J57f1V5e0RXn7fKJ21teZHuR/5ohtt8qs2AWFv8VzCb91AT8hMs0N/VElncFUcfsDGShub3vzMffvLn0flxAuRJ+w/aAGu7NtP6xp3sD3E81+p8gW98YVzc1REUNUeVI37Y/iyjc0E1+TTd/rGsDXbT95nDbz0NbdiDqag6PMlZlfZ4OkUwiXtSOJUhF/AD7QdL50aGfX0s5DgSuJ68bJHUbEm9r5JnA1EUvzBGBtoq7enrbPrrAZ+fiV9Grbn5VUdcNxV+CRtrcacROSOWa+tHkudbmsP7V56vvUZlKbx9xWavPs2lk02rxYnuSeKumhttsELO8/8Pnhfe9NpNuuRdLOwHGOBAP7AFsoareNu96diZPuyJYmsylu34VlxD4T4Qp1OVPFt+v4p6QtejctkrZkZp3CQSad/W4d4ILiHtJ/ozXWBCEdOGOW9sdKenLvOGlJFzfArq6Dk2zrMKJEwTOI0d+XAn+tM5jljcxexKj2G4mnOduXNsfNx4jyGAcAJ9j+dd3Ckn5ie1vNLLkh4jK0VoXp/W0/tKzj84Ror1eui1X0jt9tiPinr5bPOzM9FnA5RUSXAtfa/mjFNsx1DGDSjYlp86R0GVKbB0htDlKbx9dWajOpzW1YLH9ytwV2V9Qn+xdTB8CMUTHb242hvXfZPkLStkQWt48QB+2jxrDu2RzYsyluPzLunihhL+AISX8sn+8FvGDYgirB9p589rt9J9xeK8awH04DjlZk7LuZ5mMKurkBdnUdnGRbd7X9BUl7OmLlTlSzS1/nGxnbPy9v/0HE/MwJtteR9BDgscD7FEk1Lrb9korlty2vo5QagamMij23xcsbRHT58Stpd2A72zeXzwcQbmVVdrcqCtsPFVLbtRl7k3ljkto8p7oMqc39pDZPJ7V5rG2lNpPa3IbF4q68/rD5nipFMMxmmA/534FlVY/z+2x7Be7fT9TF+3Jv3kgdHzMaU3H7EdvcmpmJEhoTQEi6HVHcWkQJgKHJOiSdaXuL8v4o261qGS90ijvP25iZDbDpacdlwLOJ43bki0MXN8CuroNz3Zak02xvJel7wCeIDKNH2t5wlH62aOdbdd+P+wmEIh5nG+BxhGvUOkRGx9Yj04rYwmcDL7K9Q8UyvesNTL/mNN6cSboYeLRLDUhFZsbT6tzDJL2PcPf8KtPdIkcKMUkmxyS1eUXV5dK31OZFQmrz7G1Sm2vXkdrcx6J4ktsTTEl3p++i0sDDy3RM+bwD8HPgNZKOsP2hGts/SPoskaDhg+XkXdKp8xWU0bxzPUJtLM+iuH0X1DHLoaQ7Am8C1rf9SkkbSXqQ7W8PW7zv/ZzXixsyQt/fj6ZR1UnSc+fZgZbuPIVLgPPbiKiknWq+w/aM8hxdbCbdVh//LelOwJuBTxI1IfeqM5B0DPUuUcNE8dHA74h6iT+DOcna2M9P+qZP2f59GyNFJsqnE/UanwocRTwJG8osrzcfAM5SlDqBEP19G2x6yW/63Z9ahZgk88OEtXnOdRlSm/sX73uf2jxFanNqcxWpzWNmsTzJfSYRz3NvIpX/+sCFth9SY/M94LkuWfAUAfFHErW5ltn+txrbOxIH2nm2L5F0L+ChHi2WohFJhwFv9+i1sSaCpAvpkOVQ0lcJH//dbG8iaTXgpx6S3GJgtHj5+8WOpGW2t1RfsgpJJ9p+XIPd/yNuSI5lejzTjDIFkg6qWZVtv2wcNpNuqw5Je9n+WM33tfvXfSVC+myWAk8CdgE2JeLXDrf9i1H7NwqSVvdUCYG65Xp9ewpR+uKrwCdtbzDH/bsnU66kP7P957lsL5k8k9TmSelyaSu1ObV5KKnNqc1NpDaPj8XyJ/ccYsTgh8VdaTtgF9uvqrG5ENjM9k3l8x2As20/uK2L0+Do9LgFT9LxwCOIlOuta2NNCnXMcijpDNsP79/Pks6xvdmQZevcvFak0duJ0tWdRxVZ8GyPlKJ+oSLpt7bXm8P134EQrQ8TNQA/OQdtPJqoNbqG7fUkbQa82vZrK5a/jajXt7vty8u8y2yP/emMpI0dyXaG3hA3uTepY33HZH6YD22ea10ubaQ2pzYPJbV5bkhtTm0exqJwVwZutn2VpCWSltg+QdIHG2y+DJymSLUNsCNwuMLf/YI6wyGj0+sBFxE/8DhZIS9ufW4ha9Ity+FNZYTYZX0bUlFDcZZuFwuZkd15oLtgdrmAdb3oTbKtYatqtVAkjHg/M+OuhopPEdAdCBHdgLj5aXLZ6srHiJHfb5U+nSPpsTXLb0kUiP+hIi7sK0QJl7ngTcCrmJlJFxrcm9S95mIyf0xMmyeoy5DanNpcTWrzGNoatqpWC6U2d2Wl1ObF8if3muLSdBJwmKQriLT5ldh+r6TvEtkfBbzGdi87W1Mh5PcSdeGmjU7PaguG93G5e4WkdYCrRnU/miM+Mkv7dwPHAfctbl/bALvPtlOLAUnr2v69p2Kk/k5cWJC0Ywv7kevPdbmAdb3oTbKtCtqeXwcRx/FHS7t7UCHCkg4GNiHc0PazfX7HvrXG9u80vTTJrTXLngWcBbxN0jbEtez2ko4FjrZ94Bj79ary2iWT7taeqrm4n6T9mbubkWQ8TFKbJ6LLpY+pzck0UpvH11YFqc2pzTOxveAnYHUiwcQqRJD/G4kU5FXLLyEC/Lu2d0Z5PQdYUt6fPsbt2Qr4cTlINgfOB/5MjE4/dQXY3w8Athky/7HAhi3XcVdi9OwZwDrzvU0rywRcDGwwZP4ewK9a2H+fqJd4IZFQ4IvABxtszh14XQP4/rhtJtUWcB1w7ZDpOuCWlr/DsvJ6Xt+8kyuWva2se7Dd64j6cuM+Ro4kEkGcSRSAfwvwlRHXsYQYcT5ojo7jnYE1y/v/7F3rGmx+Vl5PI57W3QG4ZC76l9PYfueJafNc63JZX2pzTlX7LbV5ljapza3WkdrcNy2KJ7meCuC+TdJ3aBhVtX2bpHNU6rx1aHLk0ekR+RTwDiIl9/HA02yfJmljIgvccWNsqwsfI/o3yPXlu6GjlkN8/XvxQuuV3yJLgTSzN/ADSU+3fQmApLcTWfdqEy8UutSfW1AF5z16zblh3KjIsnqJpNcDfwDuXtHe2DO8NvAa4OPAfYDfEzdPrxtlBbZvA75XprmgS03TYTUXPzdH/UvGwIS1ea51GVKbk2pSm2dpk9rcTGrzdBb0n1xJWxHprv9GuCodQtSdWiJpN9t1gnMv4BclZmXUxBHPIk7evQn3qTsxPXX2bFnFJSOkpPfYPq307aIBN4f5YgPb5w7OtH2GpA1q7Ib5+i83J0uBNGL7u5L+BRwr6dnAK4gEKI+1fXWLVfRqHv6pxMr8EVi3wWahFpyfDXsRblhvJK492xNPquYd21fSHHIx3/RctHYA/s/2NyXtW2dg+73l7VGSvk2H+o7JZJgnbZ5rXYbU5qSC1OaxtjUbUptnx0qlzQs6u7KkM5gaVT2QgVFV12RhVEW6cQ9JM97Qh7HH46gmNf/g5/lA0qW2HzDqd8n4KKNs3wBOBZ5v+8aWds8gsvXdl6mkGPvaPqbWcMp+wRScX2hI+q+ar90nRPNOEcI/EDVNtyT+nJzu4VlcHwH8zqWMgaTdgOcCvyGO3b9NrONJK+Zbm+cqTja1OWkitXm8bS0EUpvnsL8L/E/u2S712yRdaPvBfd+1KTWwPrCR7R8qauwttX1dzfKVo9NEXbmxuCqpPjX/qrZvN452uiLpcOB4258bmP9y4Mm2X1Bh91bbHyrvd7Z9RN93/2N7mJtV0oek64jRUBFxDzcTI2+1ZRtUkmJUfLdjnZBqeGH3vxMxL1eMy2bSbXVB0rfqvm/pCTInSHrzkNmrE3Fed7W9RoXdXerWOxdCpRFqmko6E3ii7b8pMlF+BXgD8DDgwbafN+7+JbNjkto8KV0ubaU2J0NJbR5fW11IbR4PK502ewKBv/M1AWcOez/s8xDbVwI/pyQEADYCftRgcwbwZCIw+2pgqzJ/Y+Cs+d4fE9zv9yBGKX9MuDntD5wI/BS451z8XjnN+jfrnBSDKJD+N+CoMl1V5l0CvGRcNpNuq+N+/CvhdrUPkczlcf3TfP/Off1ck0gacTnwQeDuNcteDlxWXm8Friz78Fbg8jns47bAHuX93YD7VSx3Tt/7TxMjxL3PZ8/3vs5p6G82MW1OXZ62L1KbV7IptXls+zG1eXx9XGm0ed5/0Dk+WG6lL/Ma0zOj3dxgezaR3eysvnnnNdn0vb9w4LtFJaZlm7cjRm3eAGzfYvmzhr1frPtvwr/V04uwbNQ37+3AecC6DbbHAPfo+3wPIuPeXajIhNrFZtJtddyPS4lRzoOJ1P7/DTxkvn/fvv7dpfTpcmBfYO0RbA8Ant73+WnA/nPUz3eX3+2X5fO9gVMqlj2fiIWEqHv62P7v5nuf5zT0N5uYNqcuD90nqc0ryZTaPLb9mNo8nn6uVNq8oBNPeXbFyP9l+6ZesghJq9Bch+u2vvc3DHzXZLvgsH0CcMIoJhXvh31OxohnlxRjA9t/6ft8BfBAh4vKzWO0mXRbI2P7ViKD6nElxmgX4MclCc0nx9nWqEj6MLATEQP5UNv/GHEVj7D9mt4H28dKmqtYoecQJVjOLG39UVJVZs3DiUyjVxLX3ZMBJD2AcH1LVjAmrM2pywOkNq88pDaPh9TmsbFSafOC/pM7S06U9A5gNUlPAl5LjF7UsZmkaynxOOU95fOq1WZJIfffPGL7R5J2J1zZTgWe4HZJMU4uyQh6cVrPBU6StDpwzRhtJt1WJ4qA7kCI6AbAJ5hU4fN63gz8i3CFeqemsr3WxoT1caWk/wQOJW5sdyVco+aCm2xbkgHKbzUU2++T9CMi6+73XYaJiZjLN8xR/5L5Y1RtTl2ZPbkP55HU5vGQ2jwWViptXtCJp2aDoo7Wy4lYHhE1pz7v3GHJAqRrUoxiK0KctinL/wQ4qu5c6WIz6ba6IOlgYBPgWKKI+/njbmO+KEku3k3EM0HUG93Pc5Pc4i1ErOWTgPcDLyOy7n5i3G0lKxepzcliIrV5PKQ2j62tlUqb809ukiTJmJB0G1O1O/svrm1HZJNCeUq3/I+M7R/Mc5eSJEmSlZDU5vGxMmlz/smtQNI2RPD3+oRbd+9EuP989itJVjRKGYAPAncnzpM2I8wj20y6rSSQdAw1cXeeQOkFSUuBF9o+bK7bSlZsUpuTpB2pzQub1OZm8k9uBZIuAvYGlhGuIQDYnis/9yRZKZF0KbCj7Qvn0mbSbSWBpMfVfW/7xDG2tRbwOuA+wLeAH5TP+xBZcp81rraSlZPU5iRpR2rzwia1uZlMPFXN320fO9+dSJKVgL90EKkuNpNuK2FKKCWtCjyAGDn+VcvEJ6NyCFHL9KdEFtF9iHIxz7J99hy0l6x8pDYnSTtSmxcwqc3N5JPcCiR9gKir9XUi6xkAts+ct04lyQqIpI8D9wS+wfRzpTJrYRebSbeVBKVEy/8QCSZ+Q2RGXBc4CHin7bGVepB0nu2HlvdLieL269m+blxtJCs3qc1J0o7U5oVNanMz+SS3mkeV14f3zTOw/Tz0JUlWZNYCricSEfQw9an5u9hMuq0k+DCwJnC/nqAV16WPlGnPMba1XJRt3yrp8hVdRJOJk9qcJO1IbV7YpDY3kE9ykyRJkkokXQI8cLCsQxnNvcj2RmNs61amMmAKWI24CcqEJEmSJElSSG1uJp/kVlCKRj+XKBi9fD/Zfs989SlJVkRKPMjLgYcAq/bm237ZOG0m3VayHA+rW1hGc8c6Smp76TjXlyw8UpuTpB2pzQue1OYGlsx3B1Zgvgk8C7iFGL3oTUmSTOcQIq7mKcCJRExIkxtLF5tJt5UEF0jabXCmpF2Bi+ahP8niJrU5SdqR2rywSW1uIN2VK5B0vu1N5rsfSbKiI+ks25tLOtf2ppJuRxQIr4yR62Iz6baSQNJ9iBipG4iyLQYeQbgrPcf2H+axe8kiI7U5SdqR2rywSW1uJt2VqzlV0kNtnzffHUmSFZxeQoJrJG0C/JlwJRy3zaTbSoAilI+StD3hVibgWNs/mt+eJYuU1OYkaUdq8wImtbmZ/JNbzbbA7pIuJ1Kb94KrN53fbiXJCseBktYG3kUUCV8D+K85sJl0W0kfto8Hjp/vfiSLntTmJGlHavMiILW5mnRXrkDS+sPm2/7NpPuSJEmSJElqc5IkSdKOfJJbQU8wJd2dvqxvSZJMR9Kdgd2Yme30jeO0mXRbSZKseKQ2J0k7UpuTxU7+ya1A0jOB/YF7A1cA6wMXEn7vSZJM8V3gNOA84LY5tJl0W0mSrGCkNidJa1Kbk0VNuitXIOkcYHvghyX723bALrZfNc9dS5IVCkln2t5irm0m3VaSJCseqc1J0o7U5mSxk39yK5B0hu2HF0Hd3PZtkk63/cj57luSrEhI2hv4B/BtIhEMALb/Nk6bSbeVJMmKR2pzkrQjtTlZ7KS7cjXXSFoDOAk4TNIVRPH5JEmmcxPwYeCdRJ02yuv9x2wz6baSJFnxSG1OknakNieLmnySW4Gk1YkCy0uAFwN3Ag6zfdW8dixJVjAk/Qp4lCSaIdwAAAfKSURBVO0r59Jm0m0lSbLikdqcJO1IbU4WO/kktwLb/yxvb5P0HeAq54hAkgzjF8D1E7CZdFtJkqxgpDYnSWtSm5NFTf7JHUDSVsAHgL8B7wUOAdYBlkjazfZx89m/JFkBuRU4W9IJTI+rqSsD0MVm0m0lSbKCkNqcJCOT2pwsavJP7kw+BbyDcIE6Hnia7dMkbQwcDqSQJsl0vlGmubaZdFtJkqw4pDYnyWikNieLmozJHUDS2bYfVt5faPvBfd+dZXvz+etdkiRJkiw+UpuTJEmSUcgnuTPpL0h9w8B3OSKQJANI2gh4P/BvwKq9+bYrMyR2sZl0W0mSrFCkNifJCKQ2J4udJfPdgRWQzSRdK+k6YNPyvvf5ofPduSRZATkI+D+ijMd2wJeIeLlx20y6rSRJVhxSm5NkNFKbk0VNuisnSTIrJC2zvaWk82w/tMw72fZjxmkz6baSJEmSZGUltTlZ7KS7cpIks+VGSUuASyS9HvgDcPc5sJl0W0mSJEmyspLanCxq8klukiSzQtIjgAuBOxOlPdYCPmz7tHHaTLqtJEmSJFlZSW1OFjv5JzdJks5IWgp8wPY+c2kz6baSJEmSZGUltTlJMvFUkiSzwPatwJaSNJc2k24rSZIkSVZWUpuTJGNykySZPWcB35R0BPDP3kzbXx+zzaTbSpIkSZKVldTmZFGTf3KTJJktdwGuArbvm2egTqi62Ey6rSRJkiRZWUltThY1GZObJEmSJEmSJEmSLBgyJjdJklkhaV1JR0u6QtJfJB0lad1x20y6rSRJkiRZWUltThY7+Sc3SZLZchDwLeDewH2AY8q8cdtMuq0kSZIkWVlJbU4WNemunCTJrJB0tu2HNc2brc2k20qSJEmSlZXU5mSxk09ykySZLVdK2lXS0jLtSiSTGLfNpNtKkiRJkpWV1OZkUZNPcpMkmRWS1gM+BTyayIx4KvBG278dp82k20qSJEmSlZXU5mSxk39ykyTphKR1bf++4rsdbR8zDptJt5UkSZIkKyupzUkSpLtykiRd+ZGkDQZnStoD+NgYbSbdVpIkSZKsrKQ2Jwn5JzdJku7sDfxA0ka9GZLeDrwJeNwYbSbdVpIkSZKsrKQ2Jwmwynx3IEmSlRPb35X0L+BYSc8GXgE8Anis7avHZTPptpIkSZJkZSW1OUmCjMlNkmRWSNoW+AaRNOL5tm+cC5tJt5UkSZIkKyupzcliJ//kJknSCUnXERkRBdwBuBm4tXy27bXGYTPptpIkSZJkZSW1OUmC/JObJEmSJEmSJEmSLBgy8VSSJEmSJEmSJEmyYMg/uUmSJEmSJEmSJMmCIf/kJkmSJEmSJEmSJAuG/JObJEmSJEmSJEmSLBjyT26SzCOS/lFe7y3pyJrl7izptR3Wv6+kt9R8/x5JT2yxnmdL+q+GZU4trxtIelGLdT5D0n5NyyVJkiTJJEltTm1OVn4yu3KSzCOS/mF7jRbLbQB82/YmI65/X+Aftj/SqYNT6zkVeKbtK1ss+3jgLbaf0bCcgDOBbWxfP5v+JUmSJMm4SG1ObU5WfvJJbpKsAJQR1vPL+4dIOl3S2ZLOlf5/e/cSGlcVx3H8+yM+qhiXFiuoaFsDSklduFAitEJXPiiIuhIpLnSlRcViV91ooasUxI1iEQz4LFRcVCtoSylSE2sbEDfi2kfB+ggF69/FnOAwtsbEJM0k3w9cZubM/57z3wx/zrln7s06YDdwc2vb0+KeS3K8xezq6mtnkm+SHAJumWHcfUkebO+/S7IryUSSU0mGWvt64Ox0EU2yOsn+JF+1487W/mvrdjcw0nLdnuRIkuGuMY8m2VCdFbZPgX8tuJIkXQzWZql/XXKxE5D0D08Ao1X1ZpLLgAFgB3BbVQ0DJNkCrAPuoPMA9QNJ7gZ+Ax4BNtL5fU8A47MY+8equr1tv3oWeBy4q/UzbS/wWVVtTTIA9K5276BrtTjJaeAx4OlWlC+vqpMt9gtgBHh7FjlKkrTYrM1SH/FKrrT0HANeSPI8cENVTZ0nZks7vqRT5IboFNYRYH9V/V5VZ4ADsxz7/fY6DtzY3l8L/NAVsxl4BaCqzlXVzzP0+Q5wb5JLgW3Avq7vvgfWzDJHSZIWm7VZ6iNOcqUlpqrGgPuBKeBgks3nCQvwUlUNt2NtVb023cX/GP5sez3H3zs9poBVc+2w/afnY+AB4CFgrOvrVa1/SZKWLGuz1F+c5EpLTJKbgG+rai+d1d4NwC/AYFfYQWBbkqvaOdcluQY4DGxNckWSQeC+eUjpa2Bt1+dPgCfbuANJru6J780V4FU6W6mOV9Xprvb1wOQ85ChJ0oKxNkv9xUmutPQ8DEwmOUFnq9MbVfUTcDTJZJI9VfURnVXXY0lOAe8Cg1U1AbwFnADeA47MQz6HgY3tjosATwGb2rjjwK098SeBP9qNL7YDVNU4cAZ4vSd2E/DhPOQoSdJCsjZLfcRHCEmaUZJR4IOqOjTH89fQuVvjUFX92dpWA2NVdc+8JSpJ0gphbZYuzCu5kv6LF4Er53JikkeBz4Gd00W0uR54Zh5ykyRpJbI2SxfglVxpBUjyMp3HDXQbrareLUqSJGkRWJulheMkV5IkSZK0bLhdWZIkSZK0bDjJlSRJkiQtG05yJUmSJEnLhpNcSZIkSdKy8RcywIXFiPidzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "s0 = sns.countplot(df[\"listed_in(city)\"], ax=axs[0])\n",
    "s0.set_title(\"Counts\")\n",
    "s1 = sns.barplot(x=\"listed_in(city)\", y = \"transformed_rate\", data=df, ax=axs[1])\n",
    "s1.set_ylim(0.6, 1)\n",
    "s1.set_title(\"Rating by restaurant location\")\n",
    "s1.set_xticklabels(s1.get_xticklabels(), rotation=90)\n",
    "s0.set_xticklabels(s0.get_xticklabels(), rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the ratings that stand out are the lower ones with the restaurants in Electronic City having the lowest ratings overall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Restaurant Rate Prediction**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we can use some regression models in order to predict a restaurant rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>rest_type</th>\n",
       "      <th>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Cafe, Casual Dining</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Quick Bites</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Casual Dining</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  online_order book_table            rest_type  \\\n",
       "0          Yes        Yes        Casual Dining   \n",
       "1          Yes         No        Casual Dining   \n",
       "2          Yes         No  Cafe, Casual Dining   \n",
       "3           No         No          Quick Bites   \n",
       "4           No         No        Casual Dining   \n",
       "\n",
       "                         cuisines  approx_cost(for two people)  \\\n",
       "0  North Indian, Mughlai, Chinese                        800.0   \n",
       "1     Chinese, North Indian, Thai                        800.0   \n",
       "2          Cafe, Mexican, Italian                        800.0   \n",
       "3      South Indian, North Indian                        300.0   \n",
       "4        North Indian, Rajasthani                        600.0   \n",
       "\n",
       "  listed_in(type) listed_in(city)  transformed_rate  \n",
       "0          Buffet    Banashankari              0.82  \n",
       "1          Buffet    Banashankari              0.82  \n",
       "2          Buffet    Banashankari              0.76  \n",
       "3          Buffet    Banashankari              0.74  \n",
       "4          Buffet    Banashankari              0.76  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df = df.copy()\n",
    "pred_df.drop(columns=\"votes\", inplace=True)\n",
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split the lists in rest_type and cuisines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Casual Dining\n",
       "1          Casual Dining\n",
       "2    Cafe, Casual Dining\n",
       "3            Quick Bites\n",
       "4          Casual Dining\n",
       "Name: rest_type, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df[\"rest_type\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>cuisines</th>\n",
       "      <th>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Beverage Shop</th>\n",
       "      <th>...</th>\n",
       "      <th>Irani Cafee</th>\n",
       "      <th>Kiosk</th>\n",
       "      <th>Lounge</th>\n",
       "      <th>Meat Shop</th>\n",
       "      <th>Mess</th>\n",
       "      <th>Microbrewery</th>\n",
       "      <th>Pub</th>\n",
       "      <th>Quick Bites</th>\n",
       "      <th>Sweet Shop</th>\n",
       "      <th>Takeaway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>North Indian, Mughlai, Chinese</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Chinese, North Indian, Thai</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Cafe, Mexican, Italian</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>South Indian, North Indian</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>North Indian, Rajasthani</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  online_order book_table                        cuisines  \\\n",
       "0          Yes        Yes  North Indian, Mughlai, Chinese   \n",
       "1          Yes         No     Chinese, North Indian, Thai   \n",
       "2          Yes         No          Cafe, Mexican, Italian   \n",
       "3           No         No      South Indian, North Indian   \n",
       "4           No         No        North Indian, Rajasthani   \n",
       "\n",
       "   approx_cost(for two people) listed_in(type) listed_in(city)  \\\n",
       "0                        800.0          Buffet    Banashankari   \n",
       "1                        800.0          Buffet    Banashankari   \n",
       "2                        800.0          Buffet    Banashankari   \n",
       "3                        300.0          Buffet    Banashankari   \n",
       "4                        600.0          Buffet    Banashankari   \n",
       "\n",
       "   transformed_rate  Bakery  Bar  Beverage Shop  ...  Irani Cafee  Kiosk  \\\n",
       "0              0.82       0    0              0  ...            0      0   \n",
       "1              0.82       0    0              0  ...            0      0   \n",
       "2              0.76       0    0              0  ...            0      0   \n",
       "3              0.74       0    0              0  ...            0      0   \n",
       "4              0.76       0    0              0  ...            0      0   \n",
       "\n",
       "   Lounge  Meat Shop  Mess  Microbrewery  Pub  Quick Bites  Sweet Shop  \\\n",
       "0       0          0     0             0    0            0           0   \n",
       "1       0          0     0             0    0            0           0   \n",
       "2       0          0     0             0    0            0           0   \n",
       "3       0          0     0             0    0            1           0   \n",
       "4       0          0     0             0    0            0           0   \n",
       "\n",
       "   Takeaway  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df[\"rest_type\"] = pred_df[\"rest_type\"].apply(lambda x: x.split(\", \"))\n",
    "rt = pred_df[\"rest_type\"]\n",
    "mlb = MultiLabelBinarizer()\n",
    "rts = pd.DataFrame(mlb.fit_transform(rt), columns=mlb.classes_, index=pred_df.index)\n",
    "pred_df = pd.concat([pred_df, rts], axis=1)\n",
    "pred_df.drop(columns=\"rest_type\", inplace=True)\n",
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    North Indian, Mughlai, Chinese\n",
       "1       Chinese, North Indian, Thai\n",
       "2            Cafe, Mexican, Italian\n",
       "3        South Indian, North Indian\n",
       "4          North Indian, Rajasthani\n",
       "Name: cuisines, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df[\"cuisines\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>approx_cost(for two people)</th>\n",
       "      <th>listed_in(type)</th>\n",
       "      <th>listed_in(city)</th>\n",
       "      <th>transformed_rate</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Beverage Shop</th>\n",
       "      <th>Bhojanalya</th>\n",
       "      <th>...</th>\n",
       "      <th>Sushi</th>\n",
       "      <th>Tamil</th>\n",
       "      <th>Tea</th>\n",
       "      <th>Tex-Mex</th>\n",
       "      <th>Thai</th>\n",
       "      <th>Tibetan</th>\n",
       "      <th>Turkish</th>\n",
       "      <th>Vegan</th>\n",
       "      <th>Vietnamese</th>\n",
       "      <th>Wraps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>800.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Buffet</td>\n",
       "      <td>Banashankari</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  online_order book_table  approx_cost(for two people) listed_in(type)  \\\n",
       "0          Yes        Yes                        800.0          Buffet   \n",
       "1          Yes         No                        800.0          Buffet   \n",
       "2          Yes         No                        800.0          Buffet   \n",
       "3           No         No                        300.0          Buffet   \n",
       "4           No         No                        600.0          Buffet   \n",
       "\n",
       "  listed_in(city)  transformed_rate  Bakery  Bar  Beverage Shop  Bhojanalya  \\\n",
       "0    Banashankari              0.82       0    0              0           0   \n",
       "1    Banashankari              0.82       0    0              0           0   \n",
       "2    Banashankari              0.76       0    0              0           0   \n",
       "3    Banashankari              0.74       0    0              0           0   \n",
       "4    Banashankari              0.76       0    0              0           0   \n",
       "\n",
       "   ...  Sushi  Tamil  Tea  Tex-Mex  Thai  Tibetan  Turkish  Vegan  Vietnamese  \\\n",
       "0  ...      0      0    0        0     0        0        0      0           0   \n",
       "1  ...      0      0    0        0     1        0        0      0           0   \n",
       "2  ...      0      0    0        0     0        0        0      0           0   \n",
       "3  ...      0      0    0        0     0        0        0      0           0   \n",
       "4  ...      0      0    0        0     0        0        0      0           0   \n",
       "\n",
       "   Wraps  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df[\"cuisines\"] = pred_df[\"cuisines\"].apply(lambda x: x.split(\", \"))\n",
    "rt = pred_df[\"cuisines\"]\n",
    "mlb = MultiLabelBinarizer()\n",
    "rts = pd.DataFrame(mlb.fit_transform(rt), columns=mlb.classes_, index=pred_df.index)\n",
    "pred_df = pd.concat([pred_df, rts], axis=1)\n",
    "pred_df.drop(columns=\"cuisines\", inplace=True)\n",
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's deal with the remaining categorical features now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   online_order  book_table\n",
       "0             1           1\n",
       "1             1           0\n",
       "2             1           0\n",
       "3             0           0\n",
       "4             0           0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df[\"online_order\"] = pred_df.online_order.eq('Yes').mul(1)\n",
    "pred_df[\"book_table\"] = pred_df.book_table.eq('Yes').mul(1)\n",
    "pred_df[[\"online_order\",\"book_table\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>approx_cost(for two people)</th>\n",
       "      <th>transformed_rate</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Beverage Shop</th>\n",
       "      <th>Bhojanalya</th>\n",
       "      <th>Cafe</th>\n",
       "      <th>Casual Dining</th>\n",
       "      <th>...</th>\n",
       "      <th>listed_in(city)_Lavelle Road</th>\n",
       "      <th>listed_in(city)_MG Road</th>\n",
       "      <th>listed_in(city)_Malleshwaram</th>\n",
       "      <th>listed_in(city)_Marathahalli</th>\n",
       "      <th>listed_in(city)_New BEL Road</th>\n",
       "      <th>listed_in(city)_Old Airport Road</th>\n",
       "      <th>listed_in(city)_Rajajinagar</th>\n",
       "      <th>listed_in(city)_Residency Road</th>\n",
       "      <th>listed_in(city)_Sarjapur Road</th>\n",
       "      <th>listed_in(city)_Whitefield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 170 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   online_order  book_table  approx_cost(for two people)  transformed_rate  \\\n",
       "0             1           1                        800.0              0.82   \n",
       "1             1           0                        800.0              0.82   \n",
       "2             1           0                        800.0              0.76   \n",
       "3             0           0                        300.0              0.74   \n",
       "4             0           0                        600.0              0.76   \n",
       "\n",
       "   Bakery  Bar  Beverage Shop  Bhojanalya  Cafe  Casual Dining  ...  \\\n",
       "0       0    0              0           0     0              1  ...   \n",
       "1       0    0              0           0     0              1  ...   \n",
       "2       0    0              0           0     1              1  ...   \n",
       "3       0    0              0           0     0              0  ...   \n",
       "4       0    0              0           0     0              1  ...   \n",
       "\n",
       "   listed_in(city)_Lavelle Road  listed_in(city)_MG Road  \\\n",
       "0                             0                        0   \n",
       "1                             0                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "\n",
       "   listed_in(city)_Malleshwaram  listed_in(city)_Marathahalli  \\\n",
       "0                             0                             0   \n",
       "1                             0                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   listed_in(city)_New BEL Road  listed_in(city)_Old Airport Road  \\\n",
       "0                             0                                 0   \n",
       "1                             0                                 0   \n",
       "2                             0                                 0   \n",
       "3                             0                                 0   \n",
       "4                             0                                 0   \n",
       "\n",
       "   listed_in(city)_Rajajinagar  listed_in(city)_Residency Road  \\\n",
       "0                            0                               0   \n",
       "1                            0                               0   \n",
       "2                            0                               0   \n",
       "3                            0                               0   \n",
       "4                            0                               0   \n",
       "\n",
       "   listed_in(city)_Sarjapur Road  listed_in(city)_Whitefield  \n",
       "0                              0                           0  \n",
       "1                              0                           0  \n",
       "2                              0                           0  \n",
       "3                              0                           0  \n",
       "4                              0                           0  \n",
       "\n",
       "[5 rows x 170 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df = pd.get_dummies(pred_df)\n",
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to deal with two some duplicated column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Bakery', 'Cafe'], dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.columns[pred_df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_repl = [\"Bakery\", \"Cafe\"]\n",
    "cols = []\n",
    "count = 1\n",
    "for column in pred_df.columns:\n",
    "    if column in cols_repl:\n",
    "        cols.append(column+str(count))\n",
    "        count += 1\n",
    "        continue\n",
    "    cols.append(column)\n",
    "pred_df.columns = cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.columns.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split our X and Y and its training and testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 6\n",
    "X = pred_df.drop(\"transformed_rate\", axis=1)\n",
    "y = pred_df[\"transformed_rate\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test different models using cross-validation in order to find out the best base model without any optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"linear\": LinearRegression(),\n",
    "    \"ridge\": Ridge(),\n",
    "    \"lassi\": Lasso(random_state = seed),\n",
    "    \"elastic\": ElasticNet(random_state = seed),\n",
    "    \"svr\": SVR(gamma=\"scale\"),\n",
    "    \"rforest\": RandomForestRegressor(random_state = seed, n_estimators=100),\n",
    "    \"xgb\": xgb.XGBRegressor(random_state = seed)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_validation_scores(models, X_train, y_train, cv=10, scoring=\"neg_mean_squared_error\", n_jobs=-2):\n",
    "    results = pd.DataFrame(columns=[\"Negative Mean Squared Error\", \"SD\"])\n",
    "    for name, model in models.items():\n",
    "        scores = cross_val_score(model, X_train, y_train, cv=cv, scoring=scoring, n_jobs=n_jobs)\n",
    "        score = scores.mean()\n",
    "        sd = scores.std()\n",
    "        results.loc[name, \"Negative Mean Squared Error\"] = round(score, 4)\n",
    "        results.loc[name, \"SD\"] = round(sd, 4)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_results = cross_validation_scores(models, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_2e54579e_a31b_11e9_9072_5cf3708d3a70row5_col0 {\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Negative Mean Squared Error</th>        <th class=\"col_heading level0 col1\" >SD</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70level0_row0\" class=\"row_heading level0 row0\" >linear</th>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row0_col0\" class=\"data row0 col0\" >-0.0051</td>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row0_col1\" class=\"data row0 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row1_col0\" class=\"data row1 col0\" >-0.0051</td>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row1_col1\" class=\"data row1 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70level0_row2\" class=\"row_heading level0 row2\" >lassi</th>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row2_col0\" class=\"data row2 col0\" >-0.0066</td>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row2_col1\" class=\"data row2 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70level0_row3\" class=\"row_heading level0 row3\" >elastic</th>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row3_col0\" class=\"data row3 col0\" >-0.0066</td>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row3_col1\" class=\"data row3 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70level0_row4\" class=\"row_heading level0 row4\" >svr</th>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row4_col0\" class=\"data row4 col0\" >-0.0063</td>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row4_col1\" class=\"data row4 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70level0_row5\" class=\"row_heading level0 row5\" >rforest</th>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row5_col0\" class=\"data row5 col0\" >-0.0025</td>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row5_col1\" class=\"data row5 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70level0_row6\" class=\"row_heading level0 row6\" >xgb</th>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row6_col0\" class=\"data row6 col0\" >-0.0049</td>\n",
       "                        <td id=\"T_2e54579e_a31b_11e9_9072_5cf3708d3a70row6_col1\" class=\"data row6 col1\" >0.0002</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1b4c0d51f98>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results.style.highlight_max(subset=\"Negative Mean Squared Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the Random Forest Regressor was the best model with a very good validation score. \n",
    "(Note: Using Mean Squared Error, the lowest the score the better so scikit-learn returns its negative in order to use the \"best score is the highest\" logic.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to optimize this model now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [int(x) for x in np.linspace(100, 1000, num = 10)]\n",
    "max_depth = [int(x) for x in np.linspace(10, 200, num = 20)]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "max_features = [\"sqrt\",\"log2\"]\n",
    "bootstrap = [True, False]\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'max_features': max_features,\n",
    "               'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110, 120,\n",
       "                                                      130, 140, 150, 160, 170,\n",
       "                                                      180, 190, 200, None],\n",
       "                                        'max_features': ['sqrt', 'log2'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=6, refit=True,\n",
       "                   return_train_score=False, scoring='neg_mean_squared_error',\n",
       "                   verbose=0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(random_state=seed)\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, scoring=\"neg_mean_squared_error\", n_iter = 100, cv = 3, random_state = seed, n_jobs = -2)\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for development set:\n",
      "-0.0048 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 160, 'bootstrap': True}\n",
      "-0.0048 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 170, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 110, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 90, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 110, 'bootstrap': False}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 120, 'bootstrap': True}\n",
      "-0.0044 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 20, 'bootstrap': False}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 30, 'bootstrap': False}\n",
      "-0.0044 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 130, 'bootstrap': True}\n",
      "-0.0041 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 20, 'bootstrap': False}\n",
      "-0.0047 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 190, 'bootstrap': True}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 20, 'bootstrap': True}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 190, 'bootstrap': True}\n",
      "-0.0032 (+/-0.0001) for {'n_estimators': 300, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 100, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 40, 'bootstrap': False}\n",
      "-0.0039 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 60, 'bootstrap': True}\n",
      "-0.0047 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 20, 'bootstrap': False}\n",
      "-0.0038 (+/-0.0002) for {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 60, 'bootstrap': True}\n",
      "-0.0044 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 60, 'bootstrap': True}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 70, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 70, 'bootstrap': True}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 300, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 190, 'bootstrap': False}\n",
      "-0.0036 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 30, 'bootstrap': True}\n",
      "-0.0034 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 120, 'bootstrap': True}\n",
      "-0.0044 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 150, 'bootstrap': True}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 50, 'bootstrap': False}\n",
      "-0.0036 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 40, 'bootstrap': False}\n",
      "-0.0047 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 70, 'bootstrap': True}\n",
      "-0.0032 (+/-0.0001) for {'n_estimators': 300, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 190, 'bootstrap': False}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 110, 'bootstrap': True}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 80, 'bootstrap': True}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 130, 'bootstrap': True}\n",
      "-0.0038 (+/-0.0001) for {'n_estimators': 300, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 30, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': None, 'bootstrap': True}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 60, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 190, 'bootstrap': False}\n",
      "-0.0032 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 50, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 130, 'bootstrap': False}\n",
      "-0.0038 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 190, 'bootstrap': False}\n",
      "-0.0034 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 150, 'bootstrap': True}\n",
      "-0.0044 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 20, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 90, 'bootstrap': True}\n",
      "-0.0032 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 60, 'bootstrap': False}\n",
      "-0.0050 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 10, 'bootstrap': False}\n",
      "-0.0054 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 10, 'bootstrap': False}\n",
      "-0.0041 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 70, 'bootstrap': False}\n",
      "-0.0036 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 80, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 300, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': None, 'bootstrap': False}\n",
      "-0.0036 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 150, 'bootstrap': False}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 20, 'bootstrap': True}\n",
      "-0.0034 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': None, 'bootstrap': True}\n",
      "-0.0031 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 50, 'bootstrap': False}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 130, 'bootstrap': False}\n",
      "-0.0048 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': None, 'bootstrap': True}\n",
      "-0.0038 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 160, 'bootstrap': True}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 180, 'bootstrap': False}\n",
      "-0.0048 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 190, 'bootstrap': True}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 180, 'bootstrap': True}\n",
      "-0.0036 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 70, 'bootstrap': False}\n",
      "-0.0041 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 190, 'bootstrap': False}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 110, 'bootstrap': True}\n",
      "-0.0038 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 80, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 60, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0002) for {'n_estimators': 700, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 90, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 50, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 150, 'bootstrap': True}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 70, 'bootstrap': False}\n",
      "-0.0050 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 20, 'bootstrap': True}\n",
      "-0.0050 (+/-0.0001) for {'n_estimators': 900, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 20, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 90, 'bootstrap': False}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 40, 'bootstrap': True}\n",
      "-0.0042 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 20, 'bootstrap': True}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 190, 'bootstrap': False}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 300, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 170, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 200, 'bootstrap': False}\n",
      "-0.0047 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 200, 'bootstrap': True}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 100, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 150, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 130, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0002) for {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 50, 'bootstrap': True}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 200, 'bootstrap': True}\n",
      "-0.0039 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 120, 'bootstrap': True}\n",
      "-0.0033 (+/-0.0001) for {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 40, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': None, 'bootstrap': False}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 90, 'bootstrap': False}\n",
      "-0.0036 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 80, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 100, 'bootstrap': True}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 70, 'bootstrap': False}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 170, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 20, 'bootstrap': True}\n",
      "-0.0041 (+/-0.0001) for {'n_estimators': 300, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 160, 'bootstrap': False}\n",
      "-0.0035 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 80, 'bootstrap': False}\n",
      "-0.0037 (+/-0.0001) for {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 150, 'bootstrap': True}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 100, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 120, 'bootstrap': True}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 120, 'bootstrap': False}\n",
      "-0.0041 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 100, 'bootstrap': False}\n",
      "-0.0043 (+/-0.0001) for {'n_estimators': 700, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 120, 'bootstrap': True}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 600, 'min_samples_split': 10, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 170, 'bootstrap': False}\n",
      "-0.0045 (+/-0.0001) for {'n_estimators': 1000, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 20, 'bootstrap': True}\n",
      "-0.0038 (+/-0.0001) for {'n_estimators': 400, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 160, 'bootstrap': True}\n",
      "-0.0040 (+/-0.0001) for {'n_estimators': 800, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 120, 'bootstrap': False}\n",
      "\n",
      "\n",
      "Best parameters set found:\n",
      "{'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 50, 'bootstrap': False}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Score for development set:\")\n",
    "means = rf_random.cv_results_[\"mean_test_score\"]\n",
    "stds = rf_random.cv_results_[\"std_test_score\"]\n",
    "for mean, std, params in zip(means, stds, rf_random.cv_results_[\"params\"]):\n",
    "    print(\"%0.4f (+/-%0.04f) for %r\" % (mean, std * 1.96, params))\n",
    "print()\n",
    "\n",
    "# Print best parameters\n",
    "print()\n",
    "print(\"Best parameters set found:\")\n",
    "print(rf_random.best_params_)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see by these first results, even with the best parameters, the model performs worse than the base estimator (using different folds here). As such, let's try to do a grid search with some similar values to the these parameters, as well as the base ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = [100, 200, 400, 500]\n",
    "max_depth = [40,50,60]\n",
    "max_depth.append(None)\n",
    "min_samples_split = [2, 3, 5]\n",
    "min_samples_leaf = [1, 2]\n",
    "max_features = [\"sqrt\"]\n",
    "bootstrap = [True, False]\n",
    "\n",
    "param_grid = {'n_estimators': n_estimators,\n",
    "              'max_depth': max_depth,\n",
    "              'min_samples_split': min_samples_split,\n",
    "              'min_samples_leaf': min_samples_leaf,\n",
    "              'max_features': max_features,\n",
    "              'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
       "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
       "                                             max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators='warn', n_jobs=None,\n",
       "                                             oob_score=False, random_state=6,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=-2,\n",
       "             param_grid={'bootstrap': [True, False],\n",
       "                         'max_depth': [40, 50, 60, None],\n",
       "                         'max_features': ['sqrt'], 'min_samples_leaf': [1, 2],\n",
       "                         'min_samples_split': [2, 3, 5],\n",
       "                         'n_estimators': [100, 200, 400, 500]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(random_state = seed)\n",
    "rf_grid = GridSearchCV(estimator = rf, param_grid = param_grid, scoring=\"neg_mean_squared_error\", cv = 3, n_jobs = -2)\n",
    "rf_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score for development set:\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0002) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0002) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0002) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0002) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0038 (+/-0.0001) for {'bootstrap': True, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0031 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 50, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0033 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': 60, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0034 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0033 (+/-0.0000) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0033 (+/-0.0000) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0033 (+/-0.0000) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0033 (+/-0.0000) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0032 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 400}\n",
      "-0.0035 (+/-0.0001) for {'bootstrap': False, 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "\n",
      "\n",
      "Best parameters set found:\n",
      "{'bootstrap': False, 'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 3, 'n_estimators': 500}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Score for development set:\")\n",
    "means = rf_grid.cv_results_[\"mean_test_score\"]\n",
    "stds = rf_grid.cv_results_[\"std_test_score\"]\n",
    "for mean, std, params in zip(means, stds, rf_grid.cv_results_[\"params\"]):\n",
    "    print(\"%0.4f (+/-%0.04f) for %r\" % (mean, std * 1.96, params))\n",
    "print()\n",
    "\n",
    "# Print best parameters\n",
    "print()\n",
    "print(\"Best parameters set found:\")\n",
    "print(rf_grid.best_params_)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_rf = RandomForestRegressor(random_state = seed, n_estimators=100)\n",
    "grid_rf = rf_grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_rf = RandomForestRegressor(random_state = seed, n_estimators=100)\n",
    "grid_rf = rf_grid.best_estimator_\n",
    "models = {\n",
    "    \"base_rf\": base_rf,\n",
    "    \"opt_rf\": grid_rf\n",
    "}\n",
    "cv_results_rf = cross_validation_scores(models, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_28f4e95c_a325_11e9_8343_5cf3708d3a70row0_col0 {\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_28f4e95c_a325_11e9_8343_5cf3708d3a70\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Negative Mean Squared Error</th>        <th class=\"col_heading level0 col1\" >SD</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_28f4e95c_a325_11e9_8343_5cf3708d3a70level0_row0\" class=\"row_heading level0 row0\" >base_rf</th>\n",
       "                        <td id=\"T_28f4e95c_a325_11e9_8343_5cf3708d3a70row0_col0\" class=\"data row0 col0\" >-0.0025</td>\n",
       "                        <td id=\"T_28f4e95c_a325_11e9_8343_5cf3708d3a70row0_col1\" class=\"data row0 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_28f4e95c_a325_11e9_8343_5cf3708d3a70level0_row1\" class=\"row_heading level0 row1\" >opt_rf</th>\n",
       "                        <td id=\"T_28f4e95c_a325_11e9_8343_5cf3708d3a70row1_col0\" class=\"data row1 col0\" >-0.0029</td>\n",
       "                        <td id=\"T_28f4e95c_a325_11e9_8343_5cf3708d3a70row1_col1\" class=\"data row1 col1\" >0.0001</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1b5533469e8>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results_rf.style.highlight_max(subset=\"Negative Mean Squared Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the models are very similar in performance, even if for these folds the base model has a very small advantage.\n",
    "\n",
    "In order to select the best model to use we will check how much the models overfit to the **training set**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean Squared Error for the base model is 0.0006464399763429754\n",
      "The Mean Squared Error for the optimized model is 0.001103248764156953\n"
     ]
    }
   ],
   "source": [
    "# Base Model \n",
    "base_rf.fit(X_train, y_train)\n",
    "y_base_pred = base_rf.predict(X_train)\n",
    "\n",
    "# Optimized Model \n",
    "grid_rf.fit(X_train, y_train)\n",
    "y_grid_pred = grid_rf.predict(X_train)\n",
    "\n",
    "# Using the same metric\n",
    "mse_base = mean_squared_error(y_train, y_base_pred)\n",
    "mse_grid = mean_squared_error(y_train, y_grid_pred)\n",
    "\n",
    "print(f\"The training Mean Squared Error for the base model is {mse_base}\")\n",
    "print(f\"The training Mean Squared Error for the optimized model is {mse_grid}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we have a big difference between the models for the training dataset. Given the similarity in validation scores, we will chose the model with less overfitting, that is, the **optimized** model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The test Mean Squared Error is 0.0028409858368602385 using:\n",
      "\n",
      "RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=40,\n",
      "                      max_features='sqrt', max_leaf_nodes=None,\n",
      "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                      min_samples_leaf=1, min_samples_split=3,\n",
      "                      min_weight_fraction_leaf=0.0, n_estimators=500,\n",
      "                      n_jobs=None, oob_score=False, random_state=6, verbose=0,\n",
      "                      warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# Test Scores\n",
    "y_grid_pred_test = grid_rf.predict(X_test)\n",
    "mse_grid = mean_squared_error(y_test, y_grid_pred_test)\n",
    "\n",
    "print(f\"The test Mean Squared Error is {mse_grid} using:\")\n",
    "print()\n",
    "print(grid_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = pd.DataFrame(grid_rf.feature_importances_, index = X_train.columns, columns=['importance']).sort_values('importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp = sns.barplot(y=feature_importances.index[:10], x=feature_importances[\"importance\"][:10], orient=\"h\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, we can see that the most important features are the cost, book table service, and online order service.\n",
    "\n",
    "**In conclusion, it was possible to better understand which factors influence the rating of a restaurant and develop a Random Forest model that can predict this rating with a low error.**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}