Final Notebook.ipynb

{
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    "# GOURMET GURU RESTAURANT RECOMMENDER SYSTEM\n",
    "***\n",
    "\n",
    "## 1. BUSINESS UNDERSTANDING\n",
    "***\n",
    "\n",
    "### 1.1 Overview\n",
    "\n",
    "The U.S. dining industry is rich and varied, yet locals and tourists often find it difficult to discover restaurants that meet their specific preferences. The abundance of options, without a centralized platform for personalized recommendations, complicates the decision-making process. Our system addresses this by offering tailored dining suggestions, enhancing user experience and supporting local businesses.\n",
    "\n",
    "### 1.2 Stakeholder Definition\n",
    "\n",
    "- The main stakeholder in this project is Gourmet Guru, a start up aiming to venture into the restaurant recommendation business.\n",
    "\n",
    "They have contracted us, Digital Delight to come up with a solution.\n",
    "\n",
    "### 1.3 Problem Statement\n",
    "As the dining industry expands, consumers are overwhelmed by the numerous restaurant options, making it difficult to find establishments that match their preferences. The absence of a centralized platform for personalized recommendations complicates decision-making, especially for those in unfamiliar areas. Existing solutions are too generalized and lack real-time, location-based suggestions. As the dining scene diversifies, there's an urgent need for an intelligent recommender system to provide tailored, real-time dining suggestions, enhancing the overall experience.\n",
    "\n",
    "### 1.4 Objectives\n",
    "\n",
    "#### 1.4.1 Main Objective\n",
    "\n",
    "Develop a user-friendly restaurant recommender system that provides personalized, real-time suggestions based on user location and cuisine preferences. The system will utilize various data sources to ensure accurate and relevant recommendations.\n",
    "\n",
    "#### 1.4.2 Specific Objectives\n",
    "\n",
    "- Identifying and evaluating the key attributes and factors that significantly influence restaurant ratings and customer preferences.\n",
    "- To develop a content based recommendation system that can generate personalized restaurant recommendation based on restaurant attributes.\n",
    "- To develop a collaborative based recommendation system that would generate recommendations based on prior restaurant ratings.\n",
    "- Create an intuitive, responsive web and mobile application that recommends based on content or collaborative filtering.\n",
    "\n",
    "### 1.5 Metrics of Success\n",
    "\n",
    "Metrics of Success will include:\n",
    "\n",
    "- RMSE : This will  measure the average magnitude of the errors between predicted values and actual values, providing an indication of how well a model performs. \n",
    "\n",
    "- Recommendation Accuracy: Evaluated through user feedback and satisfaction scores, focusing on how well the recommendations match user preferences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## 2. DATA UNDERSTANDING\n",
    "***\n",
    "The data used in this project was sourced by the stakeholders from [YELP](https://www.yelp.com/dataset/download). It is a collective dataset of various businesses and user information gotten from Yelp's website. It contains 6,990,280 reviews, 150,346 businesses, 200,100 pictures across 11 metropolitan areas and 19 states. \n",
    "The original data was filtered by concentrating only on restaurant businesses and on reviews made within the year and split into two datasets as shown \n",
    "[here](Preliminary_notebook.ipynb).\n",
    "\n",
    "The two datasets have information on \n",
    "> 1. Restaurant Informational Data\n",
    "> 2. User Review Data\n",
    "\n",
    "Further to the Data Understanding, the following checks on the data were performed\n",
    "\n",
    "> i. Consistency Checks\n",
    "\n",
    "> ii. Completeness Checks\n",
    "\n",
    "> iii. Validity Checks\n",
    "\n",
    "Data Understanding on the two datasets are as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import os\n",
    "import re\n",
    "import sys\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from classes.understanding import DataLoader, DataChecks, DataInfo\n",
    "from classes.EDA import UnivariateAnalysis, BivariateAnalysis, ComprehensiveEDA, UserEDA\n",
    "\n",
    "# Suppressing warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Text processing \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# Machine learning and model selection\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from surprise import Dataset, Reader, SVD, accuracy, NormalPredictor,NMF\n",
    "from surprise.model_selection import GridSearchCV, cross_validate, train_test_split \n",
    "\n",
    "# Deep learning with TensorFlow\n",
    "from tensorflow.keras import models, layers, regularizers, metrics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### i) Restaurant Informational Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the DataLoader class\n",
    "loader= DataLoader()\n",
    "\n",
    "# Instantiate the DataInfo class\n",
    "summary= DataInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RESTAURANT DATASET INFORMATION\n",
      "========================================\n",
      "\n",
      "========================================\n",
      "Shape of the dataset : (52286, 14) \n",
      "========================================\n",
      "\n",
      "========================================\n",
      "Column Names\n",
      "========================================\n",
      "Index(['business_id', 'name', 'address', 'city', 'state', 'postal_code',\n",
      "       'latitude', 'longitude', 'stars', 'review_count', 'is_open',\n",
      "       'attributes', 'categories', 'hours'],\n",
      "      dtype='object') \n",
      " \n",
      "\n",
      "========================================\n",
      "Data Summary\n",
      "========================================\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52286 entries, 0 to 52285\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   business_id   52286 non-null  object \n",
      " 1   name          52286 non-null  object \n",
      " 2   address       51843 non-null  object \n",
      " 3   city          52286 non-null  object \n",
      " 4   state         52286 non-null  object \n",
      " 5   postal_code   52265 non-null  object \n",
      " 6   latitude      52286 non-null  float64\n",
      " 7   longitude     52286 non-null  float64\n",
      " 8   stars         52286 non-null  float64\n",
      " 9   review_count  52286 non-null  int64  \n",
      " 10  is_open       52286 non-null  int64  \n",
      " 11  attributes    51720 non-null  object \n",
      " 12  categories    52286 non-null  object \n",
      " 13  hours         45007 non-null  object \n",
      "dtypes: float64(3), int64(2), object(9)\n",
      "memory usage: 5.6+ MB\n",
      "========================================\n",
      "\n",
      "========================================\n",
      "Descriptive Statistics\n",
      "========================================\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>52286.000000</td>\n",
       "      <td>52286.000000</td>\n",
       "      <td>52286.000000</td>\n",
       "      <td>52286.000000</td>\n",
       "      <td>52286.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.997663</td>\n",
       "      <td>-87.845038</td>\n",
       "      <td>3.515234</td>\n",
       "      <td>87.241078</td>\n",
       "      <td>0.669472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.010943</td>\n",
       "      <td>13.813532</td>\n",
       "      <td>0.829585</td>\n",
       "      <td>188.912445</td>\n",
       "      <td>0.470408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>27.564457</td>\n",
       "      <td>-120.083748</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.217586</td>\n",
       "      <td>-90.233506</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.484140</td>\n",
       "      <td>-86.035621</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.958370</td>\n",
       "      <td>-75.337533</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53.679197</td>\n",
       "      <td>-74.664459</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7568.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           latitude     longitude         stars  review_count       is_open\n",
       "count  52286.000000  52286.000000  52286.000000  52286.000000  52286.000000\n",
       "mean      36.997663    -87.845038      3.515234     87.241078      0.669472\n",
       "std        6.010943     13.813532      0.829585    188.912445      0.470408\n",
       "min       27.564457   -120.083748      1.000000      5.000000      0.000000\n",
       "25%       32.217586    -90.233506      3.000000     13.000000      0.000000\n",
       "50%       39.484140    -86.035621      3.500000     33.000000      1.000000\n",
       "75%       39.958370    -75.337533      4.000000     89.000000      1.000000\n",
       "max       53.679197    -74.664459      5.000000   7568.000000      1.000000"
      ]
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     "output_type": "stream",
     "text": [
      "========================================\n",
      "Dataset Overview\n",
      "========================================\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>PA</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'OutdoorSeati...</td>\n",
       "      <td>Restaurants, Food, Bubble Tea, Coffee &amp; Tea, B...</td>\n",
       "      <td>{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CF33F8-E6oudUQ46HnavjQ</td>\n",
       "      <td>Sonic Drive-In</td>\n",
       "      <td>615 S Main St</td>\n",
       "      <td>Ashland City</td>\n",
       "      <td>TN</td>\n",
       "      <td>37015</td>\n",
       "      <td>36.269593</td>\n",
       "      <td>-87.058943</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>{'BusinessParking': 'None', 'BusinessAcceptsCr...</td>\n",
       "      <td>Burgers, Fast Food, Sandwiches, Food, Ice Crea...</td>\n",
       "      <td>{'Monday': '0:0-0:0', 'Tuesday': '6:0-22:0', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k0hlBqXX-Bt0vf1op7Jr1w</td>\n",
       "      <td>Tsevi's Pub And Grill</td>\n",
       "      <td>8025 Mackenzie Rd</td>\n",
       "      <td>Affton</td>\n",
       "      <td>MO</td>\n",
       "      <td>63123</td>\n",
       "      <td>38.565165</td>\n",
       "      <td>-90.321087</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>{'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...</td>\n",
       "      <td>Pubs, Restaurants, Italian, Bars, American (Tr...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                   name            address  \\\n",
       "0  MTSW4McQd7CbVtyjqoe9mw     St Honore Pastries        935 Race St   \n",
       "1  CF33F8-E6oudUQ46HnavjQ         Sonic Drive-In      615 S Main St   \n",
       "2  k0hlBqXX-Bt0vf1op7Jr1w  Tsevi's Pub And Grill  8025 Mackenzie Rd   \n",
       "\n",
       "           city state postal_code   latitude  longitude  stars  review_count  \\\n",
       "0  Philadelphia    PA       19107  39.955505 -75.155564    4.0            80   \n",
       "1  Ashland City    TN       37015  36.269593 -87.058943    2.0             6   \n",
       "2        Affton    MO       63123  38.565165 -90.321087    3.0            19   \n",
       "\n",
       "   is_open                                         attributes  \\\n",
       "0        1  {'RestaurantsDelivery': 'False', 'OutdoorSeati...   \n",
       "1        1  {'BusinessParking': 'None', 'BusinessAcceptsCr...   \n",
       "2        0  {'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...   \n",
       "\n",
       "                                          categories  \\\n",
       "0  Restaurants, Food, Bubble Tea, Coffee & Tea, B...   \n",
       "1  Burgers, Fast Food, Sandwiches, Food, Ice Crea...   \n",
       "2  Pubs, Restaurants, Italian, Bars, American (Tr...   \n",
       "\n",
       "                                               hours  \n",
       "0  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  \n",
       "1  {'Monday': '0:0-0:0', 'Tuesday': '6:0-22:0', '...  \n",
       "2                                                NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the restaurants csv file\n",
    "restaurant_data= loader.read_data(\"data/restaurants.csv\")\n",
    "\n",
    "# Summary information on the restaurant df\n",
    "print(f'\\nRESTAURANT DATASET INFORMATION\\n' + '=='*20 + '\\n')\n",
    "summary.info(restaurant_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restaurant Dataset Column Descriptions\n",
    "***\n",
    "\n",
    "| Column Name     | Description                                                                 |\n",
    "|-----------------|-----------------------------------------------------------------------------|\n",
    "| business_id     | Unique identifier for the business.                                         |\n",
    "| name            | Name of the business.                                                       |\n",
    "| address         | Street address of the business.                                             |\n",
    "| city            | City where the business is located.                                         |\n",
    "| state           | State where the business is located.                                        |\n",
    "| postal_code     | Postal code (ZIP code) of the business location.                            |\n",
    "| latitude        | Latitude coordinate of the business location.                               |\n",
    "| longitude       | Longitude coordinate of the business location.                              |\n",
    "| stars           | Average star rating of the (typically on a scale of 1 to 5).       |\n",
    "| review_count    | Number of reviews the business has received.                                |\n",
    "| is_open         | Indicator of whether the business is currently open (1 = open, 0 = closed). |\n",
    "| attributes      | Additional attributes of the business (e.g., \"WiFi: free\", \"HasTV: true\").  |\n",
    "| categories      | List of categories the business belongs to (e.g., \"Restaurants\", \"Bars\").   |\n",
    "| hours           | Business hours (e.g., \"Monday: 8am-5pm, Tuesday: 8am-5pm\").                 |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i. Consistency Check\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO DUPLICATES FOUND\n"
     ]
    }
   ],
   "source": [
    "# Instantiating Datachecks for restaurant df\n",
    "checks= DataChecks(restaurant_data)\n",
    "\n",
    "# Checking for duplicates\n",
    "checks.check_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ii. Completeness Check\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Names</th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>Percentage Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>address</td>\n",
       "      <td>443</td>\n",
       "      <td>0.847263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>postal_code</td>\n",
       "      <td>21</td>\n",
       "      <td>0.040164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>attributes</td>\n",
       "      <td>566</td>\n",
       "      <td>1.082508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hours</td>\n",
       "      <td>7279</td>\n",
       "      <td>13.921509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Column Names  Missing Values  Percentage Missing\n",
       "0      address             443            0.847263\n",
       "1  postal_code              21            0.040164\n",
       "2   attributes             566            1.082508\n",
       "3        hours            7279           13.921509"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for null values in restaurant dataset\n",
    "checks.check_missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iii. Validity Check\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check for outliers in restaurant dataset\n",
    "checks.check_outliers_and_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Initial Observations on restaurant information data**\n",
    "***\n",
    "- > Restaurant information dataset has 52286 rows and 14 columns.\n",
    "- > All business locations are in the North West hemisphere i.e. USA based on min and max latitude and longitude.\n",
    "- > The address, \tpostalcode, attributes and hours columns contain missing values and will be addressed.\n",
    "- > There are no duplicated rows.\n",
    "- > Majority of the businesses receive between 3-4 stars on average.\n",
    "- > No significant outliers can be seen.urs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ii) User Review Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "USER DATASET INFORMATION\n",
      "========================================\n",
      "\n",
      "========================================\n",
      "Shape of the dataset : (2559586, 4) \n",
      "========================================\n",
      "\n",
      "========================================\n",
      "Column Names\n",
      "========================================\n",
      "Index(['user_id', 'business_id', 'stars', 'date'], dtype='object') \n",
      " \n",
      "\n",
      "========================================\n",
      "Data Summary\n",
      "========================================\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2559586 entries, 0 to 2559585\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Dtype \n",
      "---  ------       ----- \n",
      " 0   user_id      object\n",
      " 1   business_id  object\n",
      " 2   stars        int64 \n",
      " 3   date         object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 78.1+ MB\n",
      "========================================\n",
      "\n",
      "========================================\n",
      "Descriptive Statistics\n",
      "========================================\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>stars</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.559586e+06</td>\n",
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       "      <th>mean</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
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       "      <th>max</th>\n",
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       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "              stars\n",
       "count  2.559586e+06\n",
       "mean   3.837496e+00\n",
       "std    1.450423e+00\n",
       "min    1.000000e+00\n",
       "25%    3.000000e+00\n",
       "50%    4.000000e+00\n",
       "75%    5.000000e+00\n",
       "max    5.000000e+00"
      ]
     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "Dataset Overview\n",
      "========================================\n"
     ]
    },
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       "      <td>XQfwVwDr-v0ZS3_CbbE5Xw</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-07-07 22:09:11</td>\n",
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       "      <td>2017-01-14 20:54:15</td>\n",
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       "      <td>4</td>\n",
       "      <td>2018-07-17 03:30:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id             business_id  stars                 date\n",
       "0  mh_-eMZ6K5RLWhZyISBhwA  XQfwVwDr-v0ZS3_CbbE5Xw      3  2018-07-07 22:09:11\n",
       "1  bcjbaE6dDog4jkNY91ncLQ  e4Vwtrqf-wpJfwesgvdgxQ      4  2017-01-14 20:54:15\n",
       "2  RreNy--tOmXMl1en0wiBOg  cPepkJeRMtHapc_b2Oe_dw      4  2018-07-17 03:30:07"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the users csv file\n",
    "users_data= loader.read_data(\"data/users.csv\")\n",
    "\n",
    "# Summary information on the user review data\n",
    "print(f'\\nUSER DATASET INFORMATION\\n' + '=='*20 + '\\n')\n",
    "summary.info(users_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Users Reviews Dataset Column Descriptions\n",
    "***\n",
    "| Column Name  | Description                                                                 |\n",
    "|--------------|-----------------------------------------------------------------------------|\n",
    "| user_id      | Unique identifier for the user who wrote the review.                        |\n",
    "| business_id  | Unique identifier for the business that the review is associated with.      |\n",
    "| stars        | Star rating given by the user in the review (on a scale of 1 to 5).         |\n",
    "| date         | Date when the review was written.                                           |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i. Consistency Check\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>988470</th>\n",
       "      <td>wAph_egpAXr9ic-A2vnINQ</td>\n",
       "      <td>fiGh8ctcvIRm4Ii2GOtoHQ</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-07-23 21:43:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997259</th>\n",
       "      <td>wAph_egpAXr9ic-A2vnINQ</td>\n",
       "      <td>fiGh8ctcvIRm4Ii2GOtoHQ</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-07-23 21:43:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980862</th>\n",
       "      <td>xZ0ioQJ0Jyo_Uc0FzmyFQQ</td>\n",
       "      <td>O_kAd89gPls-dXda40NWzA</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-11-22 07:18:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005725</th>\n",
       "      <td>xZ0ioQJ0Jyo_Uc0FzmyFQQ</td>\n",
       "      <td>O_kAd89gPls-dXda40NWzA</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-11-22 07:18:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiating Datachecks for users df\n",
    "checks= DataChecks(users_data)\n",
    "\n",
    "# Checking for duplicates for users df\n",
    "checks.check_duplicates()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ii. Completeness Check\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO NULL VALUES FOUND\n"
     ]
    }
   ],
   "source": [
    "# Checking for null values in users df\n",
    "checks.check_missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iii. Validity Check\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO OUTLIERS FOUND\n"
     ]
    }
   ],
   "source": [
    "# check for outliers in users df\n",
    "checks.check_outliers_and_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Initial Observations on user review data**\n",
    "***\n",
    "> The user review dataset contains 2559586 rows and 4 columns\n",
    "\n",
    "> There is evidence of duplicated data and would require cleaning\n",
    "\n",
    "> There are no null values in the dataset.\n",
    "\n",
    "> Majority of users rate restaurants between 3 and 4 stars\n",
    "\n",
    "> useful, funny and cool columns do not seem to show any relevant information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Exploratory Data Analysis Using `EDA.py` Classes\n",
    "***\n",
    "\n",
    "In this section of the notebook, we will initiate the exploratory data analysis by instantiating the classes defined in our `EDA.py` script. These classes are designed to facilitate the visualization of various features within the `restaurants.csv` dataset, aiding in our understanding of the data's structure and trends.\n",
    "\n",
    "The analysis is split between:\n",
    "\n",
    "    i)  Univartiate Analysis of restaurant data\n",
    "    \n",
    "    ii) Bivariate analysis  of restaurant data\n",
    "\n",
    "    iii) Analysis on User Reviews data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append the path where the EDA modules are located to sys.path\n",
    "sys.path.append(os.path.abspath('EDA'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### i) Univariate Analysis of Restaurant data\n",
    "***\n",
    "\n",
    "We first perform our univariate EDA on the `restaurants.csv` dataset to examine each variable independently. \n",
    "\n",
    "\n",
    "- **Star Ratings (`stars`)**: Histogram of star ratings offer insights into the overall quality as perceived by customers, helping identify general satisfaction levels.\n",
    "- **State Distribution (`state`)**: Bar charts showing the number of restaurants per state reveal geographical market saturation and potential areas for business expansion.\n",
    "- **Operational Status (`is_open`)**: Pie charts of open vs closed status provide a snapshot of the business landscape, indicating the proportion of actively operating restaurants.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0pk2bZuTk5JRb59w7H27ZssW44447jDZt2hienp5GYGCg0bNnT+Pjjz92WG/t2rVGly5dDE9PT0OSMXToUIftHT9+/Lz7Mozf77LYv39/4/333zc6duxoeHh4GJdffrkxe/bscut///33RlxcnNGsWTOjRYsWxpgxY4w1a9aUu8viyZMnjbvuusu49NJLDYvF4rBPOblD37fffmsMGDDAsFqthoeHh9G5c2dj6dKlDjVld1n85z//6bC87K6I59Y789VXXxm33HKL4evra3h7exvdu3c3PvnkE6fbq8pdFrdu3Wo88sgjRufOnY2AgADDzc3NaNGihdG3b1/js88+c6gtLCw0Hn74YaNFixb2npTdjfCTTz4xOnfubHh5eRl/+tOfjCeeeML4/PPPy/W1Z8+eRseOHc87rjKVzYVjx44Zl1xyidG7d2/7+CZOnGj86U9/Mry8vIxrr73W+Oijj4yhQ4eWu2NmRXOvorssOhuzs+1mZmYaffr0Mby8vIyAgABj+PDhxvLlyw1JxjfffGMYhmH8+9//Nu69917jyiuvNLy9vQ2r1Wpcf/31xrJly6rcFwCoCxbDOM9tugAAABq4kSNH6p133tGJEyfk4eFh9nAAwI5LFgEAQKPy3HPPKTQ0VFdccYVOnz6tTz/9VK+//rr++te/EsYA1DsEMgAA0Ki4u7tr5syZOnLkiIqLixUWFqbZs2frscceM3toAFAOlywCAAAAgEm47T0AAAAAmIRABgAAAAAmIZABAAAAgEm4qUcdKi0t1dGjR+Xn51ejLwYFAAAA0DgYhqFTp04pNDTU/qX0FRWaZuPGjUZCQoLRsmVLQ5KxevXqCmtHjhxpSDLmzJnjsPy3334zHn30USMwMNDw8fExBgwYYBw+fNih5uTJk8b9999vNGvWzGjWrJlx//33G7m5uQ41Bw8eNBISEgwfHx8jMDDQGDNmjFFYWFit4zl8+LDDF7ny4MGDBw8ePHjw4MHj4n6cm03OZeoZsoKCAnXu3FkPPvig7rzzzgrrPvroI23btk2hoaHlXhs3bpw++eQTrVq1SoGBgZowYYISEhKUnp4uNzc3SdKQIUN05MgRJScnS/r9yyETExP1ySefSJJKSkrUv39/tWjRQps3b9aJEyc0dOhQGYahefPmVfl4/Pz8JEmHDx9Ws2bNqryeK9hsNqWkpCguLk7u7u6mjqUxor+uRX9di/66Fv11LfrrWvTXteiva9W3/ubn56tVq1b2jFARUwNZv3791K9fv0prfv75Zz366KP64osv1L9/f4fX8vLytGTJEr311lvq06ePJGnFihVq1aqV1q5dq/j4eO3bt0/JycnaunWrunXrJklavHixoqOjtX//foWHhyslJUXfffedDh8+bA99s2bN0rBhw/T8889XOVyVXabYrFmzehHIfHx81KxZs3oxIRsb+uta9Ne16K9r0V/Xor+uRX9di/66Vn3t7/k+ylSvP0NWWlqqxMREPfHEE+rYsWO519PT02Wz2RQXF2dfFhoaqoiICKWlpSk+Pl5btmyR1Wq1hzFJ6t69u6xWq9LS0hQeHq4tW7YoIiLC4QxcfHy8CgsLlZ6ert69ezsdX2FhoQoLC+3P8/PzJf0+GWw2W62PvzbK9m/2OBor+uta9Ne16K9r0V/Xor+uRX9di/66Vn3rb1XHUa8D2YsvvqimTZtq7NixTl/Pzs6Wh4eH/P39HZYHBwcrOzvbXhMUFFRu3aCgIIea4OBgh9f9/f3l4eFhr3Fm+vTpmjp1arnlKSkp8vHxqfzgLpDU1FSzh9Co0V/Xor+uRX9di/66Fv11LfrrWvTXtepLf8+cOVOlunobyNLT0/Xyyy9r165d1b5joWEYDus4W78mNeeaPHmyxo8fb39edp1oXFxcvbhkMTU1VbGxsfXqlG1jQX9di/66Fv11LfrrWvTXteiva9Ff16pv/S27eu586m0g++qrr5STk6PWrVvbl5WUlGjChAmaO3eufvrpJ4WEhKioqEi5ubkOZ8lycnLUo0cPSVJISIiOHTtWbvvHjx+3nxULCQnRtm3bHF7Pzc2VzWYrd+bsjzw9PeXp6Vluubu7e72YBFL9GktjRH9di/66Fv11LfrrWvTXteiva9Ff16ov/a3qGOrtF0MnJiZqz549ysjIsD9CQ0P1xBNP6IsvvpAkRUVFyd3d3eG0ZFZWljIzM+2BLDo6Wnl5edq+fbu9Ztu2bcrLy3OoyczMVFZWlr0mJSVFnp6eioqKuhCHCwAAAOAiZOoZstOnT+s///mP/fmBAweUkZGhgIAAtW7dWoGBgQ717u7uCgkJUXh4uCTJarVq+PDhmjBhggIDAxUQEKCJEycqMjLSftfF9u3bq2/fvhoxYoQWLVok6ffb3ickJNi3ExcXpw4dOigxMVEzZ87UyZMnNXHiRI0YMcL0Sw8BAAAANF6mniHbuXOnunTpoi5dukiSxo8fry5duuhvf/tblbcxZ84c3X777Ro8eLBuuOEG+fj46JNPPrF/B5kkvf3224qMjFRcXJzi4uLUqVMnvfXWW/bX3dzctGbNGnl5eemGG27Q4MGDdfvtt+ull16qu4MFAAAAgHOYeoasV69eMgyjyvU//fRTuWVeXl6aN29epV/gHBAQoBUrVlS67datW+vTTz+t8lgAAAAAoLbq7WfIAAAAAKCxI5ABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGCSpmYPAAAAAK71wu5fqlTXpLRY4ZLm7Dmh0iZV+zPxqS7NazEyAJwhAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkpgayTZs2acCAAQoNDZXFYtFHH31kf81ms+nJJ59UZGSkfH19FRoaqgceeEBHjx512EZhYaHGjBmj5s2by9fXVwMHDtSRI0ccanJzc5WYmCir1Sqr1arExET9+uuvDjWHDh3SgAED5Ovrq+bNm2vs2LEqKipy1aEDAAAAgLmBrKCgQJ07d9b8+fPLvXbmzBnt2rVLzz77rHbt2qUPP/xQ33//vQYOHOhQN27cOK1evVqrVq3S5s2bdfr0aSUkJKikpMReM2TIEGVkZCg5OVnJycnKyMhQYmKi/fWSkhL1799fBQUF2rx5s1atWqUPPvhAEyZMcN3BAwAAALjoNTVz5/369VO/fv2cvma1WpWamuqwbN68ebr++ut16NAhtW7dWnl5eVqyZIneeust9enTR5K0YsUKtWrVSmvXrlV8fLz27dun5ORkbd26Vd26dZMkLV68WNHR0dq/f7/Cw8OVkpKi7777TocPH1ZoaKgkadasWRo2bJief/55NWvWzIVdAAAAAHCxMjWQVVdeXp4sFosuvfRSSVJ6erpsNpvi4uLsNaGhoYqIiFBaWpri4+O1ZcsWWa1WexiTpO7du8tqtSotLU3h4eHasmWLIiIi7GFMkuLj41VYWKj09HT17t3b6XgKCwtVWFhof56fny/p98stbTZbXR56tZXt3+xxNFb017Xor2vRX9eiv65Ff2umSWlxteqqWi/xs6gO5q9r1bf+VnUcDSaQ/fbbb3rqqac0ZMgQ+xmr7OxseXh4yN/f36E2ODhY2dnZ9pqgoKBy2wsKCnKoCQ4Odnjd399fHh4e9hpnpk+frqlTp5ZbnpKSIh8fn+odoIuce5YRdYv+uhb9dS3661r017Xob/WEV7M+7Gh6lWs/O3L+Gjhi/rpWfenvmTNnqlTXIAKZzWbTPffco9LSUi1YsOC89YZhyGKx2J//8b9rU3OuyZMna/z48fbn+fn5atWqleLi4ky/zNFmsyk1NVWxsbFyd3c3dSyNEf11LfrrWvTXteiva9Hfmpmz50SV6pqUFivsaLp+CI1SaZOq/Zn4eKfA2gztosL8da361t+yq+fOp94HMpvNpsGDB+vAgQNat26dQ9AJCQlRUVGRcnNzHc6S5eTkqEePHvaaY8eOldvu8ePH7WfFQkJCtG3bNofXc3NzZbPZyp05+yNPT095enqWW+7u7l4vJoFUv8bSGNFf16K/rkV/XYv+uhb9rZ6qhqs/1ld1HX4O1cf8da360t+qjqFefw9ZWRj74YcftHbtWgUGOv4LTFRUlNzd3R1OS2ZlZSkzM9MeyKKjo5WXl6ft27fba7Zt26a8vDyHmszMTGVlZdlrUlJS5OnpqaioKFceIgAAAICLmKlnyE6fPq3//Oc/9ucHDhxQRkaGAgICFBoaqrvuuku7du3Sp59+qpKSEvvnuQICAuTh4SGr1arhw4drwoQJCgwMVEBAgCZOnKjIyEj7XRfbt2+vvn37asSIEVq0aJEkaeTIkUpISFB4+O9XVMfFxalDhw5KTEzUzJkzdfLkSU2cOFEjRoww/dJDAAAAAI2XqYFs586dDncwLPs81tChQ5WUlKSPP/5YknTNNdc4rLd+/Xr16tVLkjRnzhw1bdpUgwcP1tmzZxUTE6Nly5bJzc3NXv/2229r7Nix9rsxDhw40OG7z9zc3LRmzRqNHj1aN9xwg7y9vTVkyBC99NJLrjhsAAAAAJBkciDr1auXDMOo8PXKXivj5eWlefPmad68eRXWBAQEaMWKFZVup3Xr1vr000/Puz8AAAAAqCv1/qYeAACg8Xhh9y+1Wr9JabHC9ftdA8+96cRTXZrXatsAYIZ6fVMPAAAAAGjMCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASUwPZpk2bNGDAAIWGhspiseijjz5yeN0wDCUlJSk0NFTe3t7q1auX9u7d61BTWFioMWPGqHnz5vL19dXAgQN15MgRh5rc3FwlJibKarXKarUqMTFRv/76q0PNoUOHNGDAAPn6+qp58+YaO3asioqKXHHYAAAAACDJ5EBWUFCgzp07a/78+U5fnzFjhmbPnq358+drx44dCgkJUWxsrE6dOmWvGTdunFavXq1Vq1Zp8+bNOn36tBISElRSUmKvGTJkiDIyMpScnKzk5GRlZGQoMTHR/npJSYn69++vgoICbd68WatWrdIHH3ygCRMmuO7gAQAAAFz0mpq58379+qlfv35OXzMMQ3PnztUzzzyjQYMGSZKWL1+u4OBgrVy5UqNGjVJeXp6WLFmit956S3369JEkrVixQq1atdLatWsVHx+vffv2KTk5WVu3blW3bt0kSYsXL1Z0dLT279+v8PBwpaSk6LvvvtPhw4cVGhoqSZo1a5aGDRum559/Xs2aNXM6xsLCQhUWFtqf5+fnS5JsNptsNlvdNKmGyvZv9jgaK/rrWvTXteiva9HfyjUpLa6T9Z1th55XrKp9r6y/FaHvVcf7g2vVt/5WdRymBrLKHDhwQNnZ2YqLi7Mv8/T0VM+ePZWWlqZRo0YpPT1dNpvNoSY0NFQRERFKS0tTfHy8tmzZIqvVag9jktS9e3dZrValpaUpPDxcW7ZsUUREhD2MSVJ8fLwKCwuVnp6u3r17Ox3j9OnTNXXq1HLLU1JS5OPjUxdtqLXU1FSzh9Co0V/Xor+uRX9di/46F15H2wk7ml5u2WdHnBRCUvX77qy/FaHv1cf7g2vVl/6eOXOmSnX1NpBlZ2dLkoKDgx2WBwcH6+DBg/YaDw8P+fv7l6spWz87O1tBQUHlth8UFORQc+5+/P395eHhYa9xZvLkyRo/frz9eX5+vlq1aqW4uLgKz6pdKDabTampqYqNjZW7u7upY2mM6K9r0V/Xor+uRX8rN2fPiVqt36S0WGFH0/VDaJRKmzj+GfN4p8Babbsxq2rfK+tvReh71fH+4Fr1rb9lV8+dT70NZGUsFovDc8Mwyi0717k1zuprUnMuT09PeXp6llvu7u5eLyaBVL/G0hjRX9eiv65Ff12L/jpX1T/yq7Kdc7dFvytW3b47629F6Hv18f7gWvWlv1UdQ7297X1ISIgklTtDlZOTYz+bFRISoqKiIuXm5lZac+zYsXLbP378uEPNufvJzc2VzWYrd+YMAAAAAOpKvQ1kbdu2VUhIiMM1oEVFRdq4caN69OghSYqKipK7u7tDTVZWljIzM+010dHRysvL0/bt2+0127ZtU15enkNNZmamsrKy7DUpKSny9PRUVFSUS48TAAAAwMXL1EsWT58+rf/85z/25wcOHFBGRoYCAgLUunVrjRs3TtOmTVNYWJjCwsI0bdo0+fj4aMiQIZIkq9Wq4cOHa8KECQoMDFRAQIAmTpyoyMhI+10X27dvr759+2rEiBFatGiRJGnkyJFKSEhQePjvH3GNi4tThw4dlJiYqJkzZ+rkyZOaOHGiRowYYfpnwQAAAAA0XqYGsp07dzrcwbDsBhlDhw7VsmXLNGnSJJ09e1ajR49Wbm6uunXrppSUFPn5+dnXmTNnjpo2barBgwfr7NmziomJ0bJly+Tm5mavefvttzV27Fj73RgHDhzo8N1nbm5uWrNmjUaPHq0bbrhB3t7eGjJkiF566SVXtwAAAADARczUQNarVy8ZhlHh6xaLRUlJSUpKSqqwxsvLS/PmzdO8efMqrAkICNCKFSsqHUvr1q316aefnnfMAAAAAFBX6u1nyAAAAACgsSOQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEmamj0AAAAAoLF5Yfcv5ZY1KS1WuKQ5e06otEnt/gx/qkvzWq2P+oMzZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACap14GsuLhYf/3rX9W2bVt5e3vriiuu0HPPPafS0lJ7jWEYSkpKUmhoqLy9vdWrVy/t3bvXYTuFhYUaM2aMmjdvLl9fXw0cOFBHjhxxqMnNzVViYqKsVqusVqsSExP166+/XojDBAAAAHCRqteB7MUXX9Srr76q+fPna9++fZoxY4ZmzpypefPm2WtmzJih2bNna/78+dqxY4dCQkIUGxurU6dO2WvGjRun1atXa9WqVdq8ebNOnz6thIQElZSU2GuGDBmijIwMJScnKzk5WRkZGUpMTLygxwsAAADg4lKjrwg/cOCA2rZtW9djKWfLli267bbb1L9/f0nS5ZdfrnfeeUc7d+6U9PvZsblz5+qZZ57RoEGDJEnLly9XcHCwVq5cqVGjRikvL09LlizRW2+9pT59+kiSVqxYoVatWmnt2rWKj4/Xvn37lJycrK1bt6pbt26SpMWLFys6Olr79+9XeHi4y48VAAAAwMWnRoHsqquu0s0336zhw4frrrvukpeXV12PS5J044036tVXX9X333+vdu3a6ZtvvtHmzZs1d+5cSb8Hw+zsbMXFxdnX8fT0VM+ePZWWlqZRo0YpPT1dNpvNoSY0NFQRERFKS0tTfHy8tmzZIqvVag9jktS9e3dZrValpaVVGMgKCwtVWFhof56fny9JstlsstlsddmKaivbv9njaKzor2vRX9eiv65FfyvXpLS4TtZ3th16XrGq9r2y/laEvjvnrIc16W9F6Ht59e39t6rjqFEg++abb/TGG29owoQJevTRR3X33Xdr+PDhuv7662uyuQo9+eSTysvL09VXXy03NzeVlJTo+eef17333itJys7OliQFBwc7rBccHKyDBw/aazw8POTv71+upmz97OxsBQUFldt/UFCQvcaZ6dOna+rUqeWWp6SkyMfHpxpH6jqpqalmD6FRo7+uRX9di/66Fv11rq6uOQk7ml5u2WdHnBRCUvX77qy/FaHvzlXW8+r0tyL0vWL15f33zJkzVaqrUSCLiIjQ7NmzNWPGDH3yySdatmyZbrzxRoWFhWn48OFKTExUixYtarJpB++++65WrFihlStXqmPHjsrIyNC4ceMUGhqqoUOH2ussFovDeoZhlFt2rnNrnNWfbzuTJ0/W+PHj7c/z8/PVqlUrxcXFqVmzZuc9Pley2WxKTU1VbGys3N3dTR1LY0R/XYv+uhb9dS36W7k5e07Uav0mpcUKO5quH0KjVNrE8c+YxzsF1mrbjVlV+15ZfytC351z1vOa9Lci9L28+vb+W3b13PnUaiY0bdpUd9xxh2699VYtWLBAkydP1sSJEzV58mTdfffdevHFF9WyZcsab/+JJ57QU089pXvuuUeSFBkZqYMHD2r69OkaOnSoQkJCJP1+huuP+8nJybGfNQsJCVFRUZFyc3MdzpLl5OSoR48e9ppjx46V2//x48fLnX37I09PT3l6epZb7u7uXi8mgVS/xtIY0V/Xor+uRX9di/46V9s/Qv+4nXO3Rb8rVt2+O+tvRei7c5X1rzr9rQh9r1h9ef+t6hhqdZfFnTt3avTo0WrZsqVmz56tiRMn6r///a/WrVunn3/+WbfddlttNq8zZ86oSRPHIbq5udlve9+2bVuFhIQ4nJYsKirSxo0b7WErKipK7u7uDjVZWVnKzMy010RHRysvL0/bt2+312zbtk15eXn2GgAAAACoazWK5rNnz9bSpUu1f/9+3XrrrXrzzTd166232sNT27ZttWjRIl199dW1GtyAAQP0/PPPq3Xr1urYsaN2796t2bNn66GHHpL0+2WG48aN07Rp0xQWFqawsDBNmzZNPj4+GjJkiCTJarVq+PDhmjBhggIDAxUQEKCJEycqMjLSftfF9u3bq2/fvhoxYoQWLVokSRo5cqQSEhK4wyIAAAAAl6lRIFu4cKEeeughPfjgg/bLBs/VunVrLVmypFaDmzdvnp599lmNHj1aOTk5Cg0N1ahRo/S3v/3NXjNp0iSdPXtWo0ePVm5urrp166aUlBT5+fnZa+bMmaOmTZtq8ODBOnv2rGJiYrRs2TK5ubnZa95++22NHTvWfjfGgQMHav78+bUaPwAAAABUpkaB7IcffjhvjYeHh8ONN2rCz89Pc+fOtd/m3hmLxaKkpCQlJSVVWOPl5aV58+Y5fKH0uQICArRixYpajBYAAAAAqqdGnyFbunSp/vnPf5Zb/s9//lPLly+v9aAAAAAA4GJQo0D2wgsvqHnz5uWWBwUFadq0abUeFAAAAABcDGoUyA4ePKi2bduWW96mTRsdOnSo1oMCAAAAgItBjQJZUFCQ9uzZU275N998o8BAvqQOAAAAAKqiRoHsnnvu0dixY7V+/XqVlJSopKRE69at02OPPWb/EmcAAAAAQOVqdJfFv//97zp48KBiYmLUtOnvmygtLdUDDzzAZ8gAAJA0Z88JlTap0a/Z83qqS/nPcQMAGqYa/abw8PDQu+++q//93//VN998I29vb0VGRqpNmzZ1PT4AAAAAaLRq9U937dq1U7t27epqLAAAAABwUalRICspKdGyZcv05ZdfKicnR6WlpQ6vr1u3rk4GBwAAAACNWY0C2WOPPaZly5apf//+ioiIkMViqetxAQAAAECjV6NAtmrVKr333nu69dZb63o8AAAAAHDRqNFt7z08PHTVVVfV9VgAAAAA4KJSo0A2YcIEvfzyyzIMo67HAwAAAAAXjRpdsrh582atX79en3/+uTp27Ch3d3eH1z/88MM6GRwAAAAANGY1CmSXXnqp7rjjjroeCwAAAABcVGoUyJYuXVrX4wAAAACAi06NPkMmScXFxVq7dq0WLVqkU6dOSZKOHj2q06dP19ngAAAAAKAxq9EZsoMHD6pv3746dOiQCgsLFRsbKz8/P82YMUO//fabXn311boeJwAAAAA0OjU6Q/bYY4+pa9euys3Nlbe3t335HXfcoS+//LLOBgcAAAAAjVmN77L49ddfy8PDw2F5mzZt9PPPP9fJwAAAAACgsavRGbLS0lKVlJSUW37kyBH5+fnVelAAAAAAcDGoUSCLjY3V3Llz7c8tFotOnz6tKVOm6NZbb62rsQEAAABAo1ajSxbnzJmj3r17q0OHDvrtt980ZMgQ/fDDD2revLneeeeduh4jAAAAADRKNQpkoaGhysjI0DvvvKNdu3aptLRUw4cP13333edwkw8AAAAAQMVqFMgkydvbWw899JAeeuihuhwPAAAAAFw0ahTI3nzzzUpff+CBB2o0GAAAAAC4mNQokD322GMOz202m86cOSMPDw/5+PgQyAAAAACgCmp0l8Xc3FyHx+nTp7V//37deOON3NQDAAAAAKqoRoHMmbCwML3wwgvlzp4BAAAAAJyrs0AmSW5ubjp69GhdbhIAAAAAGq0afYbs448/dnhuGIaysrI0f/583XDDDXUyMAAAAABo7GoUyG6//XaH5xaLRS1atNAtt9yiWbNm1cW4AAAAAKDRq1EgKy0tretxAAAAAMBFp04/QwYAAAAAqLoanSEbP358lWtnz55dk10AAAAAQKNXo0C2e/du7dq1S8XFxQoPD5ckff/993Jzc9O1115rr7NYLHUzSgAAAABohGoUyAYMGCA/Pz8tX75c/v7+kn7/sugHH3xQN910kyZMmFCngwQAAACAxqhGnyGbNWuWpk+fbg9jkuTv76+///3v3GURAAAAAKqoRoEsPz9fx44dK7c8JydHp06dqvWgAAAAAOBiUKNAdscdd+jBBx/U+++/ryNHjujIkSN6//33NXz4cA0aNKiuxwgAAAAAjVKNPkP26quvauLEibr//vtls9l+31DTpho+fLhmzpxZpwMEAAAAgMaqRoHMx8dHCxYs0MyZM/Xf//5XhmHoqquukq+vb12PDwAAAAAarVp9MXRWVpaysrLUrl07+fr6yjCMuhoXAAAAADR6NQpkJ06cUExMjNq1a6dbb71VWVlZkqSHH36YW94DAAAAQBXVKJA9/vjjcnd316FDh+Tj42Nffvfddys5ObnOBgcAAAAAjVmNPkOWkpKiL774QpdddpnD8rCwMB08eLBOBgYAAAAAjV2NzpAVFBQ4nBkr88svv8jT07PWgwIAAACAi0GNAtnNN9+sN9980/7cYrGotLRUM2fOVO/evetscAAAAADQmNXoksWZM2eqV69e2rlzp4qKijRp0iTt3btXJ0+e1Ndff13XYwQAAACARqlGZ8g6dOigPXv26Prrr1dsbKwKCgo0aNAg7d69W1deeWVdjxEAAAAAGqVqnyGz2WyKi4vTokWLNHXqVFeMCQAAAAAuCtU+Q+bu7q7MzExZLBZXjAcAAAAALho1umTxgQce0JIlS+p6LAAAAABwUanRTT2Kior0+uuvKzU1VV27dpWvr6/D67Nnz66TwQEAAABAY1atM2Q//vijSktLlZmZqWuvvVbNmjXT999/r927d9sfGRkZdTrAn3/+Wffff78CAwPl4+Oja665Runp6fbXDcNQUlKSQkND5e3trV69emnv3r0O2ygsLNSYMWPUvHlz+fr6auDAgTpy5IhDTW5urhITE2W1WmW1WpWYmKhff/21To8FAAAAAP6oWoEsLCxMv/zyi9avX6/169crKChIq1atsj9fv3691q1bV2eDy83N1Q033CB3d3d9/vnn+u677zRr1ixdeuml9poZM2Zo9uzZmj9/vnbs2KGQkBDFxsbq1KlT9ppx48Zp9erVWrVqlTZv3qzTp08rISFBJSUl9pohQ4YoIyNDycnJSk5OVkZGhhITE+vsWAAAAADgXNW6ZNEwDIfnn3/+uQoKCup0QH/04osvqlWrVlq6dKl92eWXX+4wnrlz5+qZZ57RoEGDJEnLly9XcHCwVq5cqVGjRikvL09LlizRW2+9pT59+kiSVqxYoVatWmnt2rWKj4/Xvn37lJycrK1bt6pbt26SpMWLFys6Olr79+9XeHi4y44RAAAAwMWrRp8hK3NuQKtrH3/8seLj4/U///M/2rhxo/70pz9p9OjRGjFihCTpwIEDys7OVlxcnH0dT09P9ezZU2lpaRo1apTS09Ptt+ovExoaqoiICKWlpSk+Pl5btmyR1Wq1hzFJ6t69u6xWq9LS0ioMZIWFhSosLLQ/z8/Pl/T7VwPYbLY67UV1le3f7HE0VvTXteiva9Ff1yrra5PSYpfvoyGqbV/K1ne2nYbcF1erat8r629F6LtzznpYk/5WhL6XV99+v1V1HNUKZBaLpdzt7l15+/sff/xRCxcu1Pjx4/X0009r+/btGjt2rDw9PfXAAw8oOztbkhQcHOywXnBwsA4ePChJys7OloeHh/z9/cvVlK2fnZ2toKCgcvsPCgqy1zgzffp0p9/FlpKSIh8fn+odrIukpqaaPYRGjf66Fv11LfrrWmFH089fVEOfHTl/TX1VV9ecOOtvQ+6Lq1W379WZv/Tducp6XhfvD/S9YvXl99uZM2eqVFftSxaHDRsmT09PSdJvv/2mP//5z+Xusvjhhx9WZ7MVKi0tVdeuXTVt2jRJUpcuXbR3714tXLhQDzzwgL3u3FBoGMZ5g+K5Nc7qz7edyZMna/z48fbn+fn5atWqleLi4tSsWbPKD87FbDabUlNTFRsbK3d3d1PH0hjRX9eiv65Ff12rrL8/hEaptEmtLkSp0OOdAl2y3Qthzp4TtVq/SWmxwo6mO+1vQ+6Lq1W175X1tyL03TlnPa9JfytC38urb7/fyq6eO59qzYShQ4c6PL///vurs3q1tWzZUh06dHBY1r59e33wwQeSpJCQEEm/n+Fq2bKlvSYnJ8d+1iwkJERFRUXKzc11OEuWk5OjHj162GuOHTtWbv/Hjx8vd/btjzw9Pe3h9I/c3d3rxSSQ6tdYGiP661r017Xor2uVNmnqskDWkH9uddUTZ/1tyH1xter2vTrzl747V1n/6uL9gb5XrL78fqvqGKo1E/54c40L4YYbbtD+/fsdln3//fdq06aNJKlt27YKCQlRamqqunTpIun370jbuHGjXnzxRUlSVFSU3N3dlZqaqsGDB0uSsrKylJmZqRkzZkiSoqOjlZeXp+3bt+v666+XJG3btk15eXn20AYAAAAAdc01/3RXRx5//HH16NFD06ZN0+DBg7V9+3a99tpreu211yT9fpnhuHHjNG3aNIWFhSksLEzTpk2Tj4+PhgwZIkmyWq0aPny4JkyYoMDAQAUEBGjixImKjIy033Wxffv26tu3r0aMGKFFixZJkkaOHKmEhATusAgAAADAZep1ILvuuuu0evVqTZ48Wc8995zatm2ruXPn6r777rPXTJo0SWfPntXo0aOVm5urbt26KSUlRX5+fvaaOXPmqGnTpho8eLDOnj2rmJgYLVu2TG5ubvaat99+W2PHjrXfjXHgwIGaP3/+hTtYAAAAABedeh3IJCkhIUEJCQkVvm6xWJSUlKSkpKQKa7y8vDRv3jzNmzevwpqAgACtWLGiNkMFAAAAgGppYvYAAAAAAOBiRSADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCRNzR4AAMB1Xtj9S7llTUqLFS5pzp4TKm1Su18DT3VpXqv1AQC42HGGDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkDSqQTZ8+XRaLRePGjbMvMwxDSUlJCg0Nlbe3t3r16qW9e/c6rFdYWKgxY8aoefPm8vX11cCBA3XkyBGHmtzcXCUmJspqtcpqtSoxMVG//vrrBTgqAAAAABerBhPIduzYoddee02dOnVyWD5jxgzNnj1b8+fP144dOxQSEqLY2FidOnXKXjNu3DitXr1aq1at0ubNm3X69GklJCSopKTEXjNkyBBlZGQoOTlZycnJysjIUGJi4gU7PgAAAAAXnwYRyE6fPq377rtPixcvlr+/v325YRiaO3eunnnmGQ0aNEgRERFavny5zpw5o5UrV0qS8vLytGTJEs2aNUt9+vRRly5dtGLFCn377bdau3atJGnfvn1KTk7W66+/rujoaEVHR2vx4sX69NNPtX//flOOGQAAAEDj19TsAVTFI488ov79+6tPnz76+9//bl9+4MABZWdnKy4uzr7M09NTPXv2VFpamkaNGqX09HTZbDaHmtDQUEVERCgtLU3x8fHasmWLrFarunXrZq/p3r27rFar0tLSFB4e7nRchYWFKiwstD/Pz8+XJNlsNtlstjo7/poo27/Z42is6K9r0d+606S0uMJlzl6rLn5G5ZX1pC76e759NES17Utl87ch98XVqtr3mrw/0HfneP+98Orb3w9VHUe9D2SrVq3Srl27tGPHjnKvZWdnS5KCg4MdlgcHB+vgwYP2Gg8PD4cza2U1ZetnZ2crKCio3PaDgoLsNc5Mnz5dU6dOLbc8JSVFPj4+5zmyCyM1NdXsITRq9Ne16G/tOf/npN+FHU2v9fY/O3L+motVXfS3Ig2575XNyepw1t+G3BdXq27fqzN/6btzvP+ap778/XDmzJkq1dXrQHb48GE99thjSklJkZeXV4V1FovF4blhGOWWnevcGmf159vO5MmTNX78ePvz/Px8tWrVSnFxcWrWrFml+3c1m82m1NRUxcbGyt3d3dSxNEb017Xob92Zs+dEuWVNSosVdjRdP4RGqbRJ7X4NPN4psFbrN0Zl87cu+luRhtx3Z3OyOiqbvw25L65W1b7X5P2BvjvH+++FV9/+fii7eu586nUgS09PV05OjqKiouzLSkpKtGnTJs2fP9/++a7s7Gy1bNnSXpOTk2M/axYSEqKioiLl5uY6nCXLyclRjx497DXHjh0rt//jx4+XO/v2R56envL09Cy33N3dvV5MAql+jaUxor+uRX9rr7Jf+KVNmtb6DwJ+PhWri/5WpCH3va564qy/Dbkvrlbdvldn/tJ353j/NU99+fuhqmOo1zf1iImJ0bfffquMjAz7o2vXrrrvvvuUkZGhK664QiEhIQ6nJYuKirRx40Z72IqKipK7u7tDTVZWljIzM+010dHRysvL0/bt2+0127ZtU15enr0GAAAAAOpavT5D5ufnp4iICIdlvr6+CgwMtC8fN26cpk2bprCwMIWFhWnatGny8fHRkCFDJElWq1XDhw/XhAkTFBgYqICAAE2cOFGRkZHq06ePJKl9+/bq27evRowYoUWLFkmSRo4cqYSEhApv6AEAAAAAtVWvA1lVTJo0SWfPntXo0aOVm5urbt26KSUlRX5+fvaaOXPmqGnTpho8eLDOnj2rmJgYLVu2TG5ubvaat99+W2PHjrXfjXHgwIGaP3/+BT8eAAAAABePBhfINmzY4PDcYrEoKSlJSUlJFa7j5eWlefPmad68eRXWBAQEaMWKFXU0SgAAAAA4v3r9GTIAAAAAaMwIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJmlq9gAAXBxe2P1LlWublBYrXNKcPSdU2qRqb1NPdWlew5EBAACYhzNkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGCSeh3Ipk+fruuuu05+fn4KCgrS7bffrv379zvUGIahpKQkhYaGytvbW7169dLevXsdagoLCzVmzBg1b95cvr6+GjhwoI4cOeJQk5ubq8TERFmtVlmtViUmJurXX3919SECAAAAuIjV60C2ceNGPfLII9q6datSU1NVXFysuLg4FRQU2GtmzJih2bNna/78+dqxY4dCQkIUGxurU6dO2WvGjRun1atXa9WqVdq8ebNOnz6thIQElZSU2GuGDBmijIwMJScnKzk5WRkZGUpMTLygxwsAAADg4tLU7AFUJjk52eH50qVLFRQUpPT0dN18880yDENz587VM888o0GDBkmSli9fruDgYK1cuVKjRo1SXl6elixZorfeekt9+vSRJK1YsUKtWrXS2rVrFR8fr3379ik5OVlbt25Vt27dJEmLFy9WdHS09u/fr/Dw8At74AAAAAAuCvU6kJ0rLy9PkhQQECBJOnDggLKzsxUXF2ev8fT0VM+ePZWWlqZRo0YpPT1dNpvNoSY0NFQRERFKS0tTfHy8tmzZIqvVag9jktS9e3dZrValpaVVGMgKCwtVWFhof56fny9JstlsstlsdXfgNVC2f7PH0VjR3+prUlpc7drqrMPPwjlnPaxJfytC38sr60ld9Pd8+2iIatuXyuZvQ+6Lq1W177z/1h3efy+8+vb3WVXH0WACmWEYGj9+vG688UZFRERIkrKzsyVJwcHBDrXBwcE6ePCgvcbDw0P+/v7lasrWz87OVlBQULl9BgUF2WucmT59uqZOnVpueUpKinx8fKpxdK6Tmppq9hAaNfpbdTU5zxx2NL3KtZ8dOX/NxaiyvlenvxWh7xWri/5WpCH3va6uOXHW34bcF1erbt95/6093n/NU1/+Pjtz5kyV6hpMIHv00Ue1Z88ebd68udxrFovF4blhGOWWnevcGmf159vO5MmTNX78ePvz/Px8tWrVSnFxcWrWrFml+3c1m82m1NRUxcbGyt3d3dSxNEb0t/rm7DlR5dompcUKO5quH0KjVNqkam9Tj3cKrOnQGjVnfa9JfytC38sre3+oi/5WpCH3vTrvBc5UNn8bcl9crap95/237vD+e+HVt7/Pyq6eO58GEcjGjBmjjz/+WJs2bdJll11mXx4SEiLp9zNcLVu2tC/PycmxnzULCQlRUVGRcnNzHc6S5eTkqEePHvaaY8eOldvv8ePHy519+yNPT095enqWW+7u7l4vJoFUv8bSGNHfqqvJL57SJk2rvB4/B+cq6191+lsR+l6xuuhvRRpy3+uqJ87625D74mrV7Tvvv7XH+6956svfZ1UdQ72+y6JhGHr00Uf14Ycfat26dWrbtq3D623btlVISIjDacmioiJt3LjRHraioqLk7u7uUJOVlaXMzEx7TXR0tPLy8rR9+3Z7zbZt25SXl2evAQAAAIC6Vq/PkD3yyCNauXKl/vWvf8nPz8/+eS6r1Spvb29ZLBaNGzdO06ZNU1hYmMLCwjRt2jT5+PhoyJAh9trhw4drwoQJCgwMVEBAgCZOnKjIyEj7XRfbt2+vvn37asSIEVq0aJEkaeTIkUpISOAOiwAAAABcpl4HsoULF0qSevXq5bB86dKlGjZsmCRp0qRJOnv2rEaPHq3c3Fx169ZNKSkp8vPzs9fPmTNHTZs21eDBg3X27FnFxMRo2bJlcnNzs9e8/fbbGjt2rP1ujAMHDtT8+fNde4AAAAAALmr1OpAZhnHeGovFoqSkJCUlJVVY4+XlpXnz5mnevHkV1gQEBGjFihU1GSYAAAAA1Ei9/gwZAAAAADRmBDIAAAAAMAmBDAAAAABMQiADAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACT1OsvhgYAAADQuLyw+xeXbLdJabHCXbJl1+IMGQAAAACYhDNkuOjUxb/KlP0LzJw9J1TaxPF/o6e6NK/19gEAAHBx4AwZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICGQAAAACYhEAGAAAAACYhkAEAAACASQhkAAAAAGASAhkAAAAAmIRABgAAAAAmIZABAAAAgEkIZAAAAABgEgIZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYJKmZg8ArjVnzwmVNqn7H/NTXZrX+TYBAACAiw1nyAAAAADAJAQyAAAAADAJgQwAAAAATEIgO8eCBQvUtm1beXl5KSoqSl999ZXZQwIAAADQSBHI/uDdd9/VuHHj9Mwzz2j37t266aab1K9fPx06dMjsoQEAAABohAhkfzB79mwNHz5cDz/8sNq3b6+5c+eqVatWWrhwodlDAwAAANAIcdv7/1NUVKT09HQ99dRTDsvj4uKUlpbmdJ3CwkIVFhban+fl5UmSTp48KZvN5rrBVoHNZtOZM2dUlJ/rktvenzhhqfNtXihF+bm13kaT0uIK+9uQe+NK1el7Zf2tCH13zlnfa9LfitD38lz9/is17L7X9j2Y99+aqWrfef+tO7z/Vqwu/hZzpqy/J06ckLu7u0v2UR2nTp2SJBmGUWmdxThfxUXi6NGj+tOf/qSvv/5aPXr0sC+fNm2ali9frv3795dbJykpSVOnTr2QwwQAAADQgBw+fFiXXXZZha9zhuwcFovjvzYYhlFuWZnJkydr/Pjx9uelpaU6efKkAgMDK1znQsnPz1erVq10+PBhNWvWzNSxNEb017Xor2vRX9eiv65Ff12L/roW/XWt+tZfwzB06tQphYaGVlpHIPs/zZs3l5ubm7Kzsx2W5+TkKDg42Ok6np6e8vT0dFh26aWXumqINdKsWbN6MSEbK/rrWvTXteiva9Ff16K/rkV/XYv+ulZ96q/Vaj1vDTf1+D8eHh6KiopSamqqw/LU1FSHSxgBAAAAoK5whuwPxo8fr8TERHXt2lXR0dF67bXXdOjQIf35z382e2gAAAAAGiEC2R/cfffdOnHihJ577jllZWUpIiJCn332mdq0aWP20KrN09NTU6ZMKXdJJeoG/XUt+uta9Ne16K9r0V/Xor+uRX9dq6H2l7ssAgAAAIBJ+AwZAAAAAJiEQAYAAAAAJiGQAQAAAIBJCGQAAAAAYBICWQO0adMmDRgwQKGhobJYLProo4/Ou87GjRsVFRUlLy8vXXHFFXr11VddP9AGqrr93bBhgywWS7nHv//97wsz4AZm+vTpuu666+Tn56egoCDdfvvt2r9//3nXYw5XTU36yxyuuoULF6pTp072Lx2Njo7W559/Xuk6zN2qq25/mbu1M336dFksFo0bN67SOuZwzVSlv8zhqktKSirXp5CQkErXaShzl0DWABUUFKhz586aP39+leoPHDigW2+9VTfddJN2796tp59+WmPHjtUHH3zg4pE2TNXtb5n9+/crKyvL/ggLC3PRCBu2jRs36pFHHtHWrVuVmpqq4uJixcXFqaCgoMJ1mMNVV5P+lmEOn99ll12mF154QTt37tTOnTt1yy236LbbbtPevXud1jN3q6e6/S3D3K2+HTt26LXXXlOnTp0qrWMO10xV+1uGOVw1HTt2dOjTt99+W2Ftg5q7Bho0Scbq1asrrZk0aZJx9dVXOywbNWqU0b17dxeOrHGoSn/Xr19vSDJyc3MvyJgam5ycHEOSsXHjxgprmMM1V5X+Modrx9/f33j99dedvsbcrb3K+svcrZlTp04ZYWFhRmpqqtGzZ0/jscceq7CWOVx91ekvc7jqpkyZYnTu3LnK9Q1p7nKG7CKwZcsWxcXFOSyLj4/Xzp07ZbPZTBpV49OlSxe1bNlSMTExWr9+vdnDaTDy8vIkSQEBARXWMIdrrir9LcMcrp6SkhKtWrVKBQUFio6OdlrD3K25qvS3DHO3eh555BH1799fffr0OW8tc7j6qtPfMszhqvnhhx8UGhqqtm3b6p577tGPP/5YYW1DmrtNzR4AXC87O1vBwcEOy4KDg1VcXKxffvlFLVu2NGlkjUPLli312muvKSoqSoWFhXrrrbcUExOjDRs26OabbzZ7ePWaYRgaP368brzxRkVERFRYxxyumar2lzlcPd9++62io6P122+/6ZJLLtHq1avVoUMHp7XM3eqrTn+Zu9W3atUq7dq1Szt27KhSPXO4eqrbX+Zw1XXr1k1vvvmm2rVrp2PHjunvf/+7evToob179yowMLBcfUOauwSyi4TFYnF4bhiG0+WovvDwcIWHh9ufR0dH6/Dhw3rppZd4Mz2PRx99VHv27NHmzZvPW8scrr6q9pc5XD3h4eHKyMjQr7/+qg8++EBDhw7Vxo0bKwwNzN3qqU5/mbvVc/jwYT322GNKSUmRl5dXlddjDldNTfrLHK66fv362f87MjJS0dHRuvLKK7V8+XKNHz/e6ToNZe5yyeJFICQkRNnZ2Q7LcnJy1LRpU6f/ooDa6969u3744Qezh1GvjRkzRh9//LHWr1+vyy67rNJa5nD1Vae/zjCHK+bh4aGrrrpKXbt21fTp09W5c2e9/PLLTmuZu9VXnf46w9ytWHp6unJychQVFaWmTZuqadOm2rhxo/7xj3+oadOmKikpKbcOc7jqatJfZ5jDVePr66vIyMgKe9WQ5i5nyC4C0dHR+uSTTxyWpaSkqGvXrnJ3dzdpVI3b7t2769Wp8PrEMAyNGTNGq1ev1oYNG9S2bdvzrsMcrrqa9NcZ5nDVGYahwsJCp68xd2uvsv46w9ytWExMTLm70j344IO6+uqr9eSTT8rNza3cOszhqqtJf51hDldNYWGh9u3bp5tuusnp6w1q7pp0MxHUwqlTp4zdu3cbu3fvNiQZs2fPNnbv3m0cPHjQMAzDeOqpp4zExER7/Y8//mj4+PgYjz/+uPHdd98ZS5YsMdzd3Y3333/frEOo16rb3zlz5hirV682vv/+eyMzM9N46qmnDEnGBx98YNYh1Gt/+ctfDKvVamzYsMHIysqyP86cOWOvYQ7XXE36yxyuusmTJxubNm0yDhw4YOzZs8d4+umnjSZNmhgpKSmGYTB3a6u6/WXu1t65dwFkDtet8/WXOVx1EyZMMDZs2GD8+OOPxtatW42EhATDz8/P+OmnnwzDaNhzl0DWAJXdIvXcx9ChQw3DMIyhQ4caPXv2dFhnw4YNRpcuXQwPDw/j8ssvNxYuXHjhB95AVLe/L774onHllVcaXl5ehr+/v3HjjTcaa9asMWfwDYCz3koyli5daq9hDtdcTfrLHK66hx56yGjTpo3h4eFhtGjRwoiJibGHBcNg7tZWdfvL3K29cwMDc7huna+/zOGqu/vuu42WLVsa7u7uRmhoqDFo0CBj79699tcb8ty1GMb/fboNAAAAAHBBcVMPAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMAmBDACAahg2bJhuv/12s4cBAGgkCGQAAJjAZrOZPQQAQD1AIAMAwIn3339fkZGR8vb2VmBgoPr06aMnnnhCy5cv17/+9S9ZLBZZLBZt2LBBkvTkk0+qXbt28vHx0RVXXKFnn33WIXQlJSXpmmuu0RtvvKErrrhCnp6eMgzD6X4KCgpMOmoAwIXW1OwBAABQ32RlZenee+/VjBkzdMcdd+jUqVP66quv9MADD+jQoUPKz8/X0qVLJUkBAQGSJD8/Py1btkyhoaH69ttvNWLECPn5+WnSpEn27f7nP//Re++9pw8++EBubm7Kzs52uh/DMEw5bgDAhUcgAwDgHFlZWSouLtagQYPUpk0bSVJkZKQkydvbW4WFhQoJCXFY569//av9vy+//HJNmDBB7777rkMgKyoq0ltvvaUWLVpIknbt2lXhfgAAFwcuWQQA4BydO3dWTEyMIiMj9T//8z9avHixcnNzK13n/fff14033qiQkBBdcsklevbZZ3Xo0CGHmjZt2tjDWE33AwBoXAhkAACcw83NTampqfr888/VoUMHzZs3T+Hh4Tpw4IDT+q1bt+qee+5Rv3799Omnn2r37t165plnVFRU5FDn6+tbq/0AABofAhkAAE5YLBbdcMMNmjp1qnbv3i0PDw+tXr1aHh4eKikpcaj9+uuv1aZNGz3zzDPq2rWrwsLCdPDgwVrtBwBwceAzZAAAnGPbtm368ssvFRcXp6CgIG3btk3Hjx9X+/bt9dtvv+mLL77Q/v37FRgYKKvVqquuukqHDh3SqlWrdN1112nNmjVVClWV7QcAcHEgkAEAcI5mzZpp06ZNmjt3rvLz89WmTRvNmjVL/fr1U9euXbVhwwZ17dpVp0+f1vr163Xbbbfp8ccf16OPPqrCwkL1799fzz77rJKSkmq8HwDAxcFicG9dAAAAADAFnyEDAAAAAJMQyAAAAADAJAQyAAAAADAJgQwAAAAATEIgAwAAAACTEMgAAAAAwCQEMgAAAAAwCYEMAAAAAExCIAMAAAAAkxDIAAAAAMAkBDIAAAAAMMn/A5/hY/x1ul7iAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the UnivariateAnalysis class with the DataFrame\n",
    "uni_analysis = UnivariateAnalysis(restaurant_data)\n",
    "\n",
    "# Plotting the histogram of review counts\n",
    "uni_analysis.plot_histogram('stars', 'Distribution of Star Ratings')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "\n",
    "Most common ratings are 4.0 and 3.5 stars, indicating general satisfaction.\n",
    "\n",
    "Ratings below 2.5 stars are uncommon, showing few very poor experiences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the bar chart for the number of restaurants by state\n",
    "uni_analysis.plot_bar_chart('state', 'Number of Restaurants by State', horizontal=True, descending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "\n",
    "Pennsylvania (PA), Florida (FL) and Tennessee (TN) boast the highest numbers of restaurants among the states, highlighting them as major markets for the restaurant industry. \n",
    "\n",
    "Conversely, there is a noticeable decrease in restaurant counts moving from left to right in the graph. States like North Carolina (NC), Colorado (CO), Hawaii (HI), and Montana (MT) demonstrate lack of a complete dataset in these regions. \n",
    "\n",
    "Understanding these patterns can help tailor location-based strategies in the restaurant recommendation system to optimize user experiences and business growth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the pie chart for the proportion of open vs closed restaurants\n",
    "uni_analysis.plot_bar_chart('is_open', 'Proportion of Open vs Closed Restaurants')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "\n",
    "- **66.9% Open Restaurants**: A significant majority of the restaurants in our dataset are currently operational. This proportion suggests a robust industry where the majority of listed entities are active, presenting numerous options for recommendations.\n",
    "\n",
    "- **33.1% Closed Restaurants**: A substantial portion of the restaurants are no longer in operation. This data is crucial for ensuring accuracy in our recommendation system by filtering out closed establishments, thereby improving user experience and system reliability.\n",
    "\n",
    "**Strategic Recommendations:**\n",
    "***\n",
    " Regular updates and verification processes are necessary to maintain the accuracy of the system, ensuring that closed restaurants are removed from active recommendation lists.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot on most common restaurant names in dataset\n",
    "uni_analysis.plot_bar_chart('name', 'Frequency of Restaurant Names', limit=20, horizontal=True, descending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "\n",
    "McDonald's, Subway, and Taco Bell lead in frequency, indicating a high presence of these chains within the dataset. This suggests a dominance of fast-food chains in the restaurant landscape.\n",
    "\n",
    "The frequency of well-known names like Wendy's, Domino's Pizza, and Pizza Hut reflects their popularity, which can be leveraged in recommending familiar choices to users of the recommendation system.\n",
    "\n",
    "**Strategic Implications**: \n",
    "***\n",
    "\n",
    "Understanding which brands are most common can help tailor marketing strategies within the recommendation system, highlighting promotions, deals, or new menu items from these popular chains."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### ii) Bivariate Analysis of the Restaurant Data\n",
    "***\n",
    "\n",
    "In this section, we use the `BivariateAnalysis` class to explore relationships between pairs of variables in the `restaurants.csv` dataset. By examining these relationships, we aim to uncover patterns and correlations that can inform better decision-making for restaurant recommendations and business strategies.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the BivariateAnalysis class with the DataFrame\n",
    "bi_analysis = BivariateAnalysis(restaurant_data)\n",
    "\n",
    "# Plotting the scatter plot to explore correlations between review counts and star ratings\n",
    "bi_analysis.plot_scatter('review_count', 'stars', 'Review Count vs Star Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "\n",
    " Higher review counts are more commonly associated with mid to high ratings (3.0 to 5.0 stars).\n",
    "\n",
    "The highest review counts appear at 4.0 and 5.0 star ratings, suggesting high customer engagement with top-rated restaurants.\n",
    "\n",
    "Restaurants with ratings between 1.0 and 2.5 stars tend to have fewer reviews, indicating less engagement or fewer customers.\n",
    "\n",
    "The vertical spread at each rating level, especially at high ratings, indicates variability in the popularity of restaurants with similar ratings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the boxplot to analyze how star ratings vary across different states\n",
    "bi_analysis.plot_boxplot('state', 'stars', 'Star Rating Distribution by State')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations \n",
    "\n",
    " The majority of states have a median star rating around 4.0, indicating generally positive reviews. States like Pennsylvania (PA), Florida (FL), and California (CA) show consistent ratings with narrow interquartile ranges (IQR), suggesting stable restaurant quality.\n",
    "\n",
    " Some states, such as Illinois (IL) and Delaware (DE), exhibit a wider IQR, indicating greater variability in restaurant ratings. \n",
    "\n",
    "### Implications for the Recommendation System\n",
    "\n",
    "- **Consistency**: States with consistent ratings are more likely to provide reliable recommendations, as users can expect a certain level of quality across restaurants.\n",
    "- **High Variation**: In states with high variability, additional filters or more personalized recommendations may be necessary to better match users with restaurants that meet their expectations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### iii) Analysis on User Reviews data\n",
    "***\n",
    "\n",
    "Next, we'll perform EDA on the users.csv dataset. The analysis will help in enhancing our understanding of the user dynamics within our platform, guiding strategic decisions and improvements.\n",
    "This analysis centered on the distribution of ratings across users and a trend analysis on frequency of reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the ComprehensiveEDA class with the users DataFrame\n",
    "eda_users = ComprehensiveEDA(users_data)\n",
    "\n",
    "# Instantiate the UserEDA class specifically designed for user-related data analysis\n",
    "user_eda = UserEDA(users_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the rating distribution\n",
    "user_eda.plot_rating_distribution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "The distribution of ratings from the users.csv dataset reveals a notable preference for higher ratings, with 5-star ratings significantly more frequent than others. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting reviews over time\n",
    "user_eda.plot_reviews_over_time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "\n",
    "1. **2017-2019 Trends**:\n",
    "   - The number of reviews generally increased with noticeable seasonal peaks typically between April and July, reflecting periods of higher customer engagement, possibly due to holidays or promotions.\n",
    "\n",
    "2. **Sharp Decline in 2020**:\n",
    "   - A dramatic drop in review counts occurred in early 2020, coinciding with the COVID-19 pandemic. The pandemic led to widespread lockdowns and restaurant closures, drastically reducing customer activity and the number of reviews.\n",
    "\n",
    "3. **Slow Recovery in 2021**:\n",
    "   - The recovery in review counts during 2021 was slow and inconsistent, not reaching pre-pandemic levels. This gradual rebound may be due to ongoing restrictions, changes in consumer behavior, and the permanent closure of some restaurants.\n",
    "\n",
    "4. **Implications**:\n",
    "   - The plot highlights the significant impact of the COVID-19 pandemic on the restaurant industry, with lasting effects that may alter consumer behavior and delay a full return to previous levels of dining activity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## 3.DATA PREPARATION\n",
    "***\n",
    "\n",
    "We will clean, and transform diverse data sources, including restaurant attributes, cuisine, and historical interactions. Ensuring data quality and consistency is essential for accurate model training. The prepared dataset forms the foundation for building a robust and personalized recommendation engine.\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### i) User Review Data\n",
    "***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>988470</th>\n",
       "      <td>wAph_egpAXr9ic-A2vnINQ</td>\n",
       "      <td>fiGh8ctcvIRm4Ii2GOtoHQ</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-07-23 21:43:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997259</th>\n",
       "      <td>wAph_egpAXr9ic-A2vnINQ</td>\n",
       "      <td>fiGh8ctcvIRm4Ii2GOtoHQ</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-07-23 21:43:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980862</th>\n",
       "      <td>xZ0ioQJ0Jyo_Uc0FzmyFQQ</td>\n",
       "      <td>O_kAd89gPls-dXda40NWzA</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-11-22 07:18:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005725</th>\n",
       "      <td>xZ0ioQJ0Jyo_Uc0FzmyFQQ</td>\n",
       "      <td>O_kAd89gPls-dXda40NWzA</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-11-22 07:18:46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking the duplicates in the user data\n",
    "checks= DataChecks(users_data)\n",
    "checks.check_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO DUPLICATES FOUND\n"
     ]
    }
   ],
   "source": [
    "# Dropping the duplicates\n",
    "users_data_cleaned= users_data.drop_duplicates()\n",
    "\n",
    "# Confirming the duplicates have been dropped\n",
    "checks= DataChecks(users_data_cleaned)\n",
    "checks.check_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mh_-eMZ6K5RLWhZyISBhwA</td>\n",
       "      <td>XQfwVwDr-v0ZS3_CbbE5Xw</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-07-07 22:09:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bcjbaE6dDog4jkNY91ncLQ</td>\n",
       "      <td>e4Vwtrqf-wpJfwesgvdgxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-01-14 20:54:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RreNy--tOmXMl1en0wiBOg</td>\n",
       "      <td>cPepkJeRMtHapc_b2Oe_dw</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-07-17 03:30:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jha0USGDMefGFRLik_xFQg</td>\n",
       "      <td>bMratNjTG5ZFEA6hVyr-xQ</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-02-19 13:32:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4hBhtCSgoxkrFgHa4YAD-w</td>\n",
       "      <td>bbEXAEFr4RYHLlZ-HFssTA</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-01-02 03:17:34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id             business_id  stars                date\n",
       "0  mh_-eMZ6K5RLWhZyISBhwA  XQfwVwDr-v0ZS3_CbbE5Xw      3 2018-07-07 22:09:11\n",
       "1  bcjbaE6dDog4jkNY91ncLQ  e4Vwtrqf-wpJfwesgvdgxQ      4 2017-01-14 20:54:15\n",
       "2  RreNy--tOmXMl1en0wiBOg  cPepkJeRMtHapc_b2Oe_dw      4 2018-07-17 03:30:07\n",
       "3  Jha0USGDMefGFRLik_xFQg  bMratNjTG5ZFEA6hVyr-xQ      5 2017-02-19 13:32:05\n",
       "4  4hBhtCSgoxkrFgHa4YAD-w  bbEXAEFr4RYHLlZ-HFssTA      5 2017-01-02 03:17:34"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the first five rows fot the user_data\n",
    "users_data_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2559584 entries, 0 to 2559585\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Dtype         \n",
      "---  ------       -----         \n",
      " 0   user_id      object        \n",
      " 1   business_id  object        \n",
      " 2   stars        int64         \n",
      " 3   date         datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(1), object(2)\n",
      "memory usage: 97.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking information\n",
    "users_data_cleaned.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Observations**\n",
    " ***\n",
    " \n",
    "> The duplicated rows were dropped in preparation for modelling.\n",
    "\n",
    "> The data has 2,559,584 rows and 4 columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ii) Restaurant Informational Data\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>PA</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'OutdoorSeati...</td>\n",
       "      <td>Restaurants, Food, Bubble Tea, Coffee &amp; Tea, B...</td>\n",
       "      <td>{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CF33F8-E6oudUQ46HnavjQ</td>\n",
       "      <td>Sonic Drive-In</td>\n",
       "      <td>615 S Main St</td>\n",
       "      <td>Ashland City</td>\n",
       "      <td>TN</td>\n",
       "      <td>37015</td>\n",
       "      <td>36.269593</td>\n",
       "      <td>-87.058943</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>{'BusinessParking': 'None', 'BusinessAcceptsCr...</td>\n",
       "      <td>Burgers, Fast Food, Sandwiches, Food, Ice Crea...</td>\n",
       "      <td>{'Monday': '0:0-0:0', 'Tuesday': '6:0-22:0', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k0hlBqXX-Bt0vf1op7Jr1w</td>\n",
       "      <td>Tsevi's Pub And Grill</td>\n",
       "      <td>8025 Mackenzie Rd</td>\n",
       "      <td>Affton</td>\n",
       "      <td>MO</td>\n",
       "      <td>63123</td>\n",
       "      <td>38.565165</td>\n",
       "      <td>-90.321087</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>{'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...</td>\n",
       "      <td>Pubs, Restaurants, Italian, Bars, American (Tr...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bBDDEgkFA1Otx9Lfe7BZUQ</td>\n",
       "      <td>Sonic Drive-In</td>\n",
       "      <td>2312 Dickerson Pike</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>TN</td>\n",
       "      <td>37207</td>\n",
       "      <td>36.208102</td>\n",
       "      <td>-86.768170</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsAttire': \"'casual'\", 'Restaurants...</td>\n",
       "      <td>Ice Cream &amp; Frozen Yogurt, Fast Food, Burgers,...</td>\n",
       "      <td>{'Monday': '0:0-0:0', 'Tuesday': '6:0-21:0', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eEOYSgkmpB90uNA7lDOMRA</td>\n",
       "      <td>Vietnamese Food Truck</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tampa Bay</td>\n",
       "      <td>FL</td>\n",
       "      <td>33602</td>\n",
       "      <td>27.955269</td>\n",
       "      <td>-82.456320</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Alcohol': \"'none'\", 'OutdoorSeating': 'None'...</td>\n",
       "      <td>Vietnamese, Food, Restaurants, Food Trucks</td>\n",
       "      <td>{'Monday': '11:0-14:0', 'Tuesday': '11:0-14:0'...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                   name              address  \\\n",
       "0  MTSW4McQd7CbVtyjqoe9mw     St Honore Pastries          935 Race St   \n",
       "1  CF33F8-E6oudUQ46HnavjQ         Sonic Drive-In        615 S Main St   \n",
       "2  k0hlBqXX-Bt0vf1op7Jr1w  Tsevi's Pub And Grill    8025 Mackenzie Rd   \n",
       "3  bBDDEgkFA1Otx9Lfe7BZUQ         Sonic Drive-In  2312 Dickerson Pike   \n",
       "4  eEOYSgkmpB90uNA7lDOMRA  Vietnamese Food Truck                  NaN   \n",
       "\n",
       "           city state postal_code   latitude  longitude  stars  review_count  \\\n",
       "0  Philadelphia    PA       19107  39.955505 -75.155564    4.0            80   \n",
       "1  Ashland City    TN       37015  36.269593 -87.058943    2.0             6   \n",
       "2        Affton    MO       63123  38.565165 -90.321087    3.0            19   \n",
       "3     Nashville    TN       37207  36.208102 -86.768170    1.5            10   \n",
       "4     Tampa Bay    FL       33602  27.955269 -82.456320    4.0            10   \n",
       "\n",
       "   is_open                                         attributes  \\\n",
       "0        1  {'RestaurantsDelivery': 'False', 'OutdoorSeati...   \n",
       "1        1  {'BusinessParking': 'None', 'BusinessAcceptsCr...   \n",
       "2        0  {'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...   \n",
       "3        1  {'RestaurantsAttire': \"'casual'\", 'Restaurants...   \n",
       "4        1  {'Alcohol': \"'none'\", 'OutdoorSeating': 'None'...   \n",
       "\n",
       "                                          categories  \\\n",
       "0  Restaurants, Food, Bubble Tea, Coffee & Tea, B...   \n",
       "1  Burgers, Fast Food, Sandwiches, Food, Ice Crea...   \n",
       "2  Pubs, Restaurants, Italian, Bars, American (Tr...   \n",
       "3  Ice Cream & Frozen Yogurt, Fast Food, Burgers,...   \n",
       "4         Vietnamese, Food, Restaurants, Food Trucks   \n",
       "\n",
       "                                               hours  \n",
       "0  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  \n",
       "1  {'Monday': '0:0-0:0', 'Tuesday': '6:0-22:0', '...  \n",
       "2                                                NaN  \n",
       "3  {'Monday': '0:0-0:0', 'Tuesday': '6:0-21:0', '...  \n",
       "4  {'Monday': '11:0-14:0', 'Tuesday': '11:0-14:0'...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking into the first five rows of the data frame\n",
    "restaurant_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_id        0\n",
       "name               0\n",
       "address          443\n",
       "city               0\n",
       "state              0\n",
       "postal_code       21\n",
       "latitude           0\n",
       "longitude          0\n",
       "stars              0\n",
       "review_count       0\n",
       "is_open            0\n",
       "attributes       566\n",
       "categories         0\n",
       "hours           7279\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values in the dataset\n",
    "restaurant_data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "***\n",
    "\n",
    "The dataset contains null values in the columns: address, postal code, attributes and hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping null values\n",
    "restaurant_data = restaurant_data.dropna(subset=['attributes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_id     0\n",
       "name            0\n",
       "address         0\n",
       "city            0\n",
       "state           0\n",
       "postal_code     0\n",
       "latitude        0\n",
       "longitude       0\n",
       "stars           0\n",
       "review_count    0\n",
       "is_open         0\n",
       "attributes      0\n",
       "categories      0\n",
       "hours           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filling missing values in the 'attributes' column with 'Unknown'\n",
    "restaurant_data[['address','postal_code','hours']]=restaurant_data[['address','postal_code','hours']].fillna('Unknown')\n",
    "\n",
    "# Verifying that all missing values have been handled\n",
    "restaurant_data.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    " There were some missing values in the `attributes`, `address`, `postal_code`, and `hours` columns.\n",
    " \n",
    " We handled the missing data in two ways:\n",
    " \n",
    " 1) **Dropping the records with null values**\n",
    "\n",
    "- Rows with missing `attributes` were removed as this column is critical for analysis.\n",
    "\n",
    "2) **Imputation**\n",
    "\n",
    "- Missing values in `address`, `postal_code`, and `hours` were imputed  with 'Unknown' to maintain data integrity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "PA     12538\n",
       "FL      8632\n",
       "TN      4297\n",
       "MO      4219\n",
       "IN      4119\n",
       "LA      3596\n",
       "NJ      3302\n",
       "AZ      2635\n",
       "AB      2355\n",
       "NV      1657\n",
       "ID      1290\n",
       "CA      1151\n",
       "IL       975\n",
       "DE       949\n",
       "NC         1\n",
       "CO         1\n",
       "HI         1\n",
       "MT         1\n",
       "XMS        1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the value counts for the states\n",
    "restaurant_data.state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# States labeled `XMS` will be filtered out, as they do not seem valid\n",
    "valid_states = ['XMS','MT','NC','HI','CO',]\n",
    "restaurant_data_filtered = restaurant_data[~restaurant_data['state'].isin(valid_states)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map state abbreviations to full state names\n",
    "state_abbreviations = {\n",
    "    'AB': 'Alberta', 'AZ': 'Arizona', 'CA': 'California', 'DE': 'Delaware', 'FL': 'Florida', 'ID': 'Idaho','IL': 'Illinois',\n",
    "    'IN': 'Indiana','LA': 'Louisiana',  'MO': 'Missouri','NV': 'Nevada', 'NJ': 'New Jersey','PA': 'Pennsylvania',  'TN': 'Tennessee', \n",
    "}\n",
    "# Replace state initials with full names\n",
    "restaurant_data_filtered['state'] = restaurant_data_filtered['state'].map(state_abbreviations)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "***\n",
    "\n",
    "- States labeled XMS,MT,NC,HI and Co were noted in the EDA for having unusually small restaurant counts, upon confirmation, they have been filtered  out as they contain only a value of one restaurant per state therefore not statistically relevant to our project.\n",
    "\n",
    "- The state abbreviations have been successfully mapped to their corresponding full state names, providing more clarity,simplicity,coherence and consistency in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FEATURE ENGINEERING\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will continue with feature engineering with the following steps\n",
    "1. Exploding the categories to refine out cuisine list.\n",
    "2. Elimiating spaces that arise from the splits.\n",
    "3. Engineering a Location column that is a combination of various features.\n",
    "4. Filtering the attributes column to keep only attributes that are true.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'OutdoorSeati...</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'OutdoorSeati...</td>\n",
       "      <td>Food</td>\n",
       "      <td>{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'OutdoorSeati...</td>\n",
       "      <td>Bubble Tea</td>\n",
       "      <td>{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'OutdoorSeati...</td>\n",
       "      <td>Coffee &amp; Tea</td>\n",
       "      <td>{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
       "      <td>St Honore Pastries</td>\n",
       "      <td>935 Race St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19107</td>\n",
       "      <td>39.955505</td>\n",
       "      <td>-75.155564</td>\n",
       "      <td>4.0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'OutdoorSeati...</td>\n",
       "      <td>Bakeries</td>\n",
       "      <td>{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                name      address          city  \\\n",
       "0  MTSW4McQd7CbVtyjqoe9mw  St Honore Pastries  935 Race St  Philadelphia   \n",
       "0  MTSW4McQd7CbVtyjqoe9mw  St Honore Pastries  935 Race St  Philadelphia   \n",
       "0  MTSW4McQd7CbVtyjqoe9mw  St Honore Pastries  935 Race St  Philadelphia   \n",
       "0  MTSW4McQd7CbVtyjqoe9mw  St Honore Pastries  935 Race St  Philadelphia   \n",
       "0  MTSW4McQd7CbVtyjqoe9mw  St Honore Pastries  935 Race St  Philadelphia   \n",
       "\n",
       "          state postal_code   latitude  longitude  stars  review_count  \\\n",
       "0  Pennsylvania       19107  39.955505 -75.155564    4.0            80   \n",
       "0  Pennsylvania       19107  39.955505 -75.155564    4.0            80   \n",
       "0  Pennsylvania       19107  39.955505 -75.155564    4.0            80   \n",
       "0  Pennsylvania       19107  39.955505 -75.155564    4.0            80   \n",
       "0  Pennsylvania       19107  39.955505 -75.155564    4.0            80   \n",
       "\n",
       "   is_open                                         attributes     categories  \\\n",
       "0        1  {'RestaurantsDelivery': 'False', 'OutdoorSeati...    Restaurants   \n",
       "0        1  {'RestaurantsDelivery': 'False', 'OutdoorSeati...           Food   \n",
       "0        1  {'RestaurantsDelivery': 'False', 'OutdoorSeati...     Bubble Tea   \n",
       "0        1  {'RestaurantsDelivery': 'False', 'OutdoorSeati...   Coffee & Tea   \n",
       "0        1  {'RestaurantsDelivery': 'False', 'OutdoorSeati...       Bakeries   \n",
       "\n",
       "                                               hours  \n",
       "0  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  \n",
       "0  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  \n",
       "0  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  \n",
       "0  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  \n",
       "0  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploding the categories column to ensure each category is handled separately\n",
    "df_exploded = restaurant_data_filtered.assign(categories=restaurant_data_filtered['categories'].str.split(',')).explode('categories')\n",
    "\n",
    "df_exploded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to standardize spacing\n",
    "def standardize_spacing(x):\n",
    "    if isinstance(x, str):\n",
    "        return re.sub(r'\\s+', ' ', x.strip())\n",
    "    return x\n",
    "\n",
    "# Apply the function to all elements in the DataFrame\n",
    "df_exploded = df_exploded.applymap(standardize_spacing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of all the Cuisine in the categories\n",
    "cuisine=[\"American(New)\", \"Mexican\",'American (Traditional)','Italian','Chinese',\n",
    "         'Japanese','Asian Fusion','Mediterranean','Southern','Cajun/Creole','Tex-Mex',\n",
    "         'Thai','Latin American','Indian','Vietnamese','Greek','Caribbean','Middle Eastern',\n",
    "         'French','Korean','Halal','Spanish','Canadian (New)','Irish','Pakistani','Hawaiian',\n",
    "         'Soul Food','German','Szechuan','African','Filipino','Lebanese','Puerto Rican','Turkish',\n",
    "         'Cantonese','British','Peruvian','Kosher','Brazilian','Pan Asian','Taiwanese','Cuban','Colombian',\n",
    "         'Ethiopian','Venezuelan','Salvadoran','Laotian','Polish','Dominican','Russian','Persian/Iranian',\n",
    "         'Afghan','Moroccan','Arabic','Portuguese','Mongolian','Argentine','Malaysian','Belgian',\n",
    "         'Honduran','Himalayan/Nepalese','Armenian','Trinidadian','Ukrainian','Australian','Egyptian']\n",
    "\n",
    "# Filtering to get restaurants with cuisine\n",
    "restaurant_exploded = df_exploded[df_exploded.categories.isin(cuisine)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k0hlBqXX-Bt0vf1op7Jr1w</td>\n",
       "      <td>Tsevi's Pub And Grill</td>\n",
       "      <td>8025 Mackenzie Rd</td>\n",
       "      <td>Affton</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>63123</td>\n",
       "      <td>38.565165</td>\n",
       "      <td>-90.321087</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>{'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...</td>\n",
       "      <td>Italian</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k0hlBqXX-Bt0vf1op7Jr1w</td>\n",
       "      <td>Tsevi's Pub And Grill</td>\n",
       "      <td>8025 Mackenzie Rd</td>\n",
       "      <td>Affton</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>63123</td>\n",
       "      <td>38.565165</td>\n",
       "      <td>-90.321087</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>{'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...</td>\n",
       "      <td>American (Traditional)</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>k0hlBqXX-Bt0vf1op7Jr1w</td>\n",
       "      <td>Tsevi's Pub And Grill</td>\n",
       "      <td>8025 Mackenzie Rd</td>\n",
       "      <td>Affton</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>63123</td>\n",
       "      <td>38.565165</td>\n",
       "      <td>-90.321087</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>{'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...</td>\n",
       "      <td>Greek</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eEOYSgkmpB90uNA7lDOMRA</td>\n",
       "      <td>Vietnamese Food Truck</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Tampa Bay</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33602</td>\n",
       "      <td>27.955269</td>\n",
       "      <td>-82.456320</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Alcohol': \"'none'\", 'OutdoorSeating': 'None'...</td>\n",
       "      <td>Vietnamese</td>\n",
       "      <td>{'Monday': '11:0-14:0', 'Tuesday': '11:0-14:0'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>il_Ro8jwPlHresjw9EGmBg</td>\n",
       "      <td>Denny's</td>\n",
       "      <td>8901 US 31 S</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>46227</td>\n",
       "      <td>39.637133</td>\n",
       "      <td>-86.127217</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsReservations': 'False', 'Restaura...</td>\n",
       "      <td>American (Traditional)</td>\n",
       "      <td>{'Monday': '6:0-22:0', 'Tuesday': '6:0-22:0', ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id                   name            address  \\\n",
       "2  k0hlBqXX-Bt0vf1op7Jr1w  Tsevi's Pub And Grill  8025 Mackenzie Rd   \n",
       "2  k0hlBqXX-Bt0vf1op7Jr1w  Tsevi's Pub And Grill  8025 Mackenzie Rd   \n",
       "2  k0hlBqXX-Bt0vf1op7Jr1w  Tsevi's Pub And Grill  8025 Mackenzie Rd   \n",
       "4  eEOYSgkmpB90uNA7lDOMRA  Vietnamese Food Truck            Unknown   \n",
       "5  il_Ro8jwPlHresjw9EGmBg                Denny's       8901 US 31 S   \n",
       "\n",
       "           city     state postal_code   latitude  longitude  stars  \\\n",
       "2        Affton  Missouri       63123  38.565165 -90.321087    3.0   \n",
       "2        Affton  Missouri       63123  38.565165 -90.321087    3.0   \n",
       "2        Affton  Missouri       63123  38.565165 -90.321087    3.0   \n",
       "4     Tampa Bay   Florida       33602  27.955269 -82.456320    4.0   \n",
       "5  Indianapolis   Indiana       46227  39.637133 -86.127217    2.5   \n",
       "\n",
       "   review_count  is_open                                         attributes  \\\n",
       "2            19        0  {'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...   \n",
       "2            19        0  {'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...   \n",
       "2            19        0  {'Caters': 'True', 'Alcohol': \"u'full_bar'\", '...   \n",
       "4            10        1  {'Alcohol': \"'none'\", 'OutdoorSeating': 'None'...   \n",
       "5            28        1  {'RestaurantsReservations': 'False', 'Restaura...   \n",
       "\n",
       "               categories                                              hours  \n",
       "2                 Italian                                            Unknown  \n",
       "2  American (Traditional)                                            Unknown  \n",
       "2                   Greek                                            Unknown  \n",
       "4              Vietnamese  {'Monday': '11:0-14:0', 'Tuesday': '11:0-14:0'...  \n",
       "5  American (Traditional)  {'Monday': '6:0-22:0', 'Tuesday': '6:0-22:0', ...  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the 'location' column to check the combined addresses\n",
    "restaurant_exploded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 38528 entries, 2 to 52285\n",
      "Data columns (total 15 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   business_id   38528 non-null  object \n",
      " 1   name          38528 non-null  object \n",
      " 2   address       38528 non-null  object \n",
      " 3   city          38528 non-null  object \n",
      " 4   state         38528 non-null  object \n",
      " 5   postal_code   38528 non-null  object \n",
      " 6   latitude      38528 non-null  float64\n",
      " 7   longitude     38528 non-null  float64\n",
      " 8   stars         38528 non-null  float64\n",
      " 9   review_count  38528 non-null  int64  \n",
      " 10  is_open       38528 non-null  int64  \n",
      " 11  attributes    38528 non-null  object \n",
      " 12  categories    38528 non-null  object \n",
      " 13  hours         38528 non-null  object \n",
      " 14  location      38528 non-null  object \n",
      "dtypes: float64(3), int64(2), object(10)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# combining the address columns into a location column\n",
    "restaurant_exploded['location']=restaurant_exploded[['city','state','address']]\\\n",
    "            .apply( lambda x: f\"State:{x['state']}, City:{x['city']}, Address:{x['address']} \", axis=1)\n",
    "\n",
    "\n",
    "# viewing the restaurant_exploded dataset information\n",
    "restaurant_exploded.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    " \n",
    "- The `categories` column have been standardized and exploded to handle multiple categories per restaurant. This transformation is necessary to accurately categorize each restaurant's offerings, which is essential for matching user preferences with the right restaurants.\n",
    "\n",
    "- The exploded DataFrame has been filtered to include only rows with specified cuisines. By narrowing down to specific cuisines, we ensure that the recommender system focuses on the most relevant restaurant types. This helps in improving precision\n",
    "\n",
    "- Combining the `state`, `city`, and `address` columns is vital for geographic-based recommendations, enabling the system to suggest restaurants within a user’s preferred or current location.\n",
    "\n",
    "- This results in a dataset with 38,528 rows of restaurants with their cuisine types and 15 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decompress(x):\n",
    "    \"\"\"\n",
    "    The function takes in a dictionary and returns only the keys that have their values not being False   \n",
    "    \"\"\"\n",
    "      \n",
    "    list_ = []\n",
    "    \n",
    "    # Check if x is a string\n",
    "    if not isinstance(x, str):\n",
    "        return ' '\n",
    "    \n",
    "    # evaluate the attributes column to convert it from a string to a dictionary\n",
    "    try:\n",
    "        data_dict = eval(x)\n",
    "    except Exception as e:\n",
    "        print(f\"Error evaluating {x}: {str(e)}\")\n",
    "        return ' '\n",
    "    \n",
    "    # iterate through the key-value pairs in the dictionary\n",
    "    for key, val in data_dict.items():\n",
    "        # check if the key is in the specified categories and if the value is not \"None\"\n",
    "        if (key in ['Ambience', 'GoodForMeal', 'BusinessParking']) and (val != \"None\"):\n",
    "            # if conditions are met, further iterate through sub-dictionary\n",
    "            try:\n",
    "                sub_dict = eval(val)\n",
    "                for key_, val_ in sub_dict.items():\n",
    "                    # if the sub-dictionary value is true, append it to the list\n",
    "                    if val_:\n",
    "                        list_.append(f'{key}{key}')\n",
    "            except Exception as e:\n",
    "                print(f\"Error evaluating {val}: {str(e)}\")\n",
    "        else:\n",
    "            # if the value is not false, append the key to the list\n",
    "            if val != 'False':\n",
    "                list_.append(key)\n",
    "    \n",
    "    # join the list of selected attribute names into a space-separated string\n",
    "    return \" \".join(list_)\n",
    "\n",
    "# create a new column 'attributes_true' in the df by applying the decompress function\n",
    "# include a condition to handle cases where attributes is 'Not-Available'\n",
    "restaurant_exploded['attributes_true'] = restaurant_exploded.attributes.apply(lambda x: decompress(x) if x != 'Not-Available' else ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Attributes Column 2    {'Caters': 'True', 'Alcohol': \"u'full_bar'\", 'RestaurantsAttire': \"u'casual'\", 'RestaurantsDelivery': 'False', 'RestaurantsTakeOut': 'True', 'HasTV': 'True', 'NoiseLevel': \"u'average'\", 'BusinessAcceptsCreditCards': 'True', 'OutdoorSeating': 'True', 'BusinessParking': \"{'garage': False, 'street': False, 'validated': False, 'lot': True, 'valet': False}\", 'Ambience': \"{'romantic': False, 'intimate': False, 'touristy': False, 'hipster': False, 'divey': False, 'classy': False, 'trendy': False, 'upscale': False, 'casual': False}\", 'RestaurantsPriceRange2': '1', 'GoodForKids': 'True', 'WiFi': \"u'free'\", 'RestaurantsReservations': 'False', 'RestaurantsGoodForGroups': 'True'}\n",
      "Name: attributes, dtype: object\n",
      "Attributes_true Column 2    Caters Alcohol RestaurantsAttire RestaurantsTakeOut HasTV NoiseLevel BusinessAcceptsCreditCards OutdoorSeating BusinessParkingBusinessParking RestaurantsPriceRange2 GoodForKids WiFi RestaurantsGoodForGroups\n",
      "Name: attributes_true, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Example to show the new column generated using a sample business_id\n",
    "# Temporarily set display options\n",
    "pd.set_option('display.max_colwidth', None)  \n",
    "\n",
    "# Display the attributes sections\n",
    "print(\"Original Attributes Column\",restaurant_data[restaurant_data.business_id=='k0hlBqXX-Bt0vf1op7Jr1w'].attributes[:1])\n",
    "print(\"Attributes_true Column\",restaurant_exploded[restaurant_exploded.business_id=='k0hlBqXX-Bt0vf1op7Jr1w'].attributes_true[:1])\n",
    "\n",
    "# Reset display options to default after viewing\n",
    "pd.reset_option('display.max_colwidth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38528, 16)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of the dataset\n",
    "restaurant_exploded.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***\n",
    "- By using the compress function, the attributes column was stripped of any attributes that were False and a new column attributes true was created that only had the true attributes of each restaurant\n",
    "\n",
    "- This results in a dataset with 38,528 rows of restaurants with their cuisine types and 16 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the UnivariateAnalysis class with the DataFrame\n",
    "uni_analysis = UnivariateAnalysis(restaurant_exploded)\n",
    "uni_analysis.plot_bar_chart('categories', 'Number of Cuisine Counts', limit=20, horizontal=True, descending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***\n",
    "\n",
    "- The majority of the restaurants mainly fall within American(Traditional), Italian, Mexican, Chinese, Japanese\n",
    "- American(Traditional) was largely dominant in the dataset after splitting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the cleaned user review data to a new CSV file\n",
    "users_data_cleaned.to_csv('data/cleaned_users_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Save the filtered and transformed restaurant data to a new CSV file\n",
    "with open('Pickled_files/restaurants_data.pkl','wb')as file:\n",
    "    pickle.dump(restaurant_exploded, file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## 4. MODELLING\n",
    "***\n",
    "\n",
    "In this section we will create a recommendation system using the datasets to solve our main problem.\n",
    "There are different types of recomendation models, in this project we will focus on three types of recommendation systems\n",
    "\n",
    "* 1. Content-Based Recommender systems\n",
    "* 2. Collaborative Filtering Systems\n",
    "* 3. Deep Neural Networks\n",
    "\n",
    "Now, in each of these categories we will compare the different models and see which ones perform best. For validation and comparison we will use the RMSE (root mean squared error) metric, that is how far is the prediction from the true value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "##### 1. CONTENT BASED FILTERING\n",
    "***\n",
    "To perfrom the content based filtering, we utilized the restaurant data dataset.\n",
    "\n",
    "The restaurant's features such as types of cuisine they offer and attribues such as WiFi, Alcohol, Happy Hour, Noise Level, Restaurants Attire, Wheelchair Accessible, Restaurants TableService etc, were able to provide information to use cosine similarity to recommend the restaurants with the closest similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 38528 entries, 2 to 52285\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   business_id      38528 non-null  object \n",
      " 1   name             38528 non-null  object \n",
      " 2   address          38528 non-null  object \n",
      " 3   city             38528 non-null  object \n",
      " 4   state            38528 non-null  object \n",
      " 5   postal_code      38528 non-null  object \n",
      " 6   latitude         38528 non-null  float64\n",
      " 7   longitude        38528 non-null  float64\n",
      " 8   stars            38528 non-null  float64\n",
      " 9   review_count     38528 non-null  int64  \n",
      " 10  is_open          38528 non-null  int64  \n",
      " 11  attributes       38528 non-null  object \n",
      " 12  categories       38528 non-null  object \n",
      " 13  hours            38528 non-null  object \n",
      " 14  location         38528 non-null  object \n",
      " 15  attributes_true  38528 non-null  object \n",
      "dtypes: float64(3), int64(2), object(11)\n",
      "memory usage: 5.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Loading the restaurant data from the pickled file\n",
    "df = pd.read_pickle('pickled_files/restaurants_data.pkl')\n",
    "\n",
    "# Overview of dataset information to understand the features we require\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing function\n",
    "def preprocess(df):\n",
    "    \"\"\"\n",
    "    Function to preprocess the data to combine the needed features into one column\n",
    "    Returns a dataframe with the combined_features columns\n",
    "    \"\"\"\n",
    "    filtered_df=df.copy()\n",
    "    # Combining the features into one column\n",
    "    filtered_df['combined_features'] = (\n",
    "                                        filtered_df['categories'] + \" \" +\n",
    "                                        filtered_df['attributes_true'] \n",
    "                                        )\n",
    "    # resetting the index\n",
    "    filtered_df = filtered_df.reset_index(drop=True)\n",
    "\n",
    "    # Return turns the filtered df\n",
    "    return filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vectorization function\n",
    "def create_feature_vectors(df):\n",
    "    \"\"\"\n",
    "    Performing vectorization of the preprocessed categorical features \n",
    "    and combining with the numerical features\n",
    "    \"\"\"\n",
    "    # Vectorize the combined text features\n",
    "    tfidf = TfidfVectorizer(stop_words='english')\n",
    "    tfidf_matrix = tfidf.fit_transform(df['combined_features'])\n",
    "    \n",
    "    # Combine the TF-IDF matrix with numerical columns\n",
    "    numerical_features = df[['stars']].values\n",
    "    combined_features = np.hstack((tfidf_matrix.toarray(), numerical_features))\n",
    "    \n",
    "    return combined_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Using the cosine similarity matrix we will now create a content-based recommendation system that offers recommendations to users based on the restaurant names or text words representing the specifications of their desired restaurant and attributes.\n",
    "\n",
    "We use the cosine similarity matrix to compare similarities between different restaurants and the customer's preferences, then pick the top n similar restaurants to recommend based on his/her input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recommendation function\n",
    "def recommendation(df, state, name=None, category=None):\n",
    "    \"\"\"\n",
    "    Creates recommendation based on name or category/cuisine using cosine similarity and filtering\n",
    "    Returns a dataframe containing name, state, city, address, stars and categories\n",
    "    \"\"\"\n",
    "    preprocessed = preprocess(df)\n",
    "    \n",
    "    def cuisines(cuisine=None, state=state):\n",
    "        \"\"\"\n",
    "        Function to filter to get the recommendations based on cuisine input\n",
    "        \"\"\"\n",
    "        preprocessed=df[df[\"state\"]==state]\n",
    "        cuisine_df = preprocessed[preprocessed['categories'] == cuisine]\n",
    "        cuisine_df_sorted = cuisine_df.sort_values(by=[\"stars\", \"city\"], ascending=False)\n",
    "        return cuisine_df_sorted[['name', 'state', 'city', 'stars', 'address', 'categories']]\n",
    "    \n",
    "    if name:\n",
    "        if name not in preprocessed['name'].values:\n",
    "            raise ValueError(f\"Restaurant with name '{name}' not found in the filtered data.\")\n",
    "\n",
    "        # Finding the index of the restaurant name\n",
    "        idx = preprocessed[preprocessed['name'] == name].index[0]\n",
    "        exclude_names = [name]\n",
    "\n",
    "        # Locating the restaurant row in the preprocessed df \n",
    "        row_to_add = preprocessed.iloc[idx]\n",
    "        \n",
    "        # convering it to a df\n",
    "        row_to_add_df = pd.DataFrame([row_to_add])     \n",
    "        \n",
    "        #generating a df for only the state i want to recommend in\n",
    "        specific_state= preprocessed[preprocessed[\"state\"] == state]\n",
    "        \n",
    "        # concatinating it to the specific state df and reseting the index\n",
    "        specific_state = pd.concat([specific_state, row_to_add_df]).reset_index(drop=True)\n",
    "        \n",
    "        # Finding the new index for the restaurant name\n",
    "        idx = specific_state[specific_state['name'] == name].index[0]\n",
    "        \n",
    "        # Creating feature vectors\n",
    "        combined_features = create_feature_vectors(specific_state)\n",
    "\n",
    "        # Finding the cosine similarity\n",
    "        cosine_sim = cosine_similarity(combined_features, combined_features)\n",
    "\n",
    "        # Finding the top indices of the restaurants to recommend\n",
    "        sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "        top_indices = [i[0] for i in sim_scores]  \n",
    "\n",
    "        # Finding the rows of the top recommended restaurants\n",
    "        recommended_restaurants = specific_state.iloc[top_indices]\n",
    "        recommended_restaurants = recommended_restaurants[~recommended_restaurants['name'].isin(exclude_names)]        \n",
    "\n",
    "        # Return a df with the required features\n",
    "        return recommended_restaurants[['name', 'state', 'city', 'stars', 'address','categories']].drop_duplicates(subset='name')[:20]\n",
    "    \n",
    "    elif category:\n",
    "        # Filter based on cuisine/cateogry\n",
    "        return cuisines(category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The content_based function uses content-based recommendation techniques to provide restaurant recommendations based on user input preferences, restaurant names, or cuisine choice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Restaurant Name: Flor Fina\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "      <th>location</th>\n",
       "      <th>attributes_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43698</th>\n",
       "      <td>e4A1XPyEEMcfXxzDQhMrDQ</td>\n",
       "      <td>Flor Fina</td>\n",
       "      <td>1412 E 7th Ave</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33605</td>\n",
       "      <td>27.960766</td>\n",
       "      <td>-82.443715</td>\n",
       "      <td>4.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Alcohol': \"u'full_bar'\", 'RestaurantsReserva...</td>\n",
       "      <td>Mediterranean</td>\n",
       "      <td>{'Monday': '11:0-23:0', 'Tuesday': '11:0-23:0'...</td>\n",
       "      <td>State:Florida, City:Tampa, Address:1412 E 7th ...</td>\n",
       "      <td>Alcohol RestaurantsReservations BusinessAccept...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43698</th>\n",
       "      <td>e4A1XPyEEMcfXxzDQhMrDQ</td>\n",
       "      <td>Flor Fina</td>\n",
       "      <td>1412 E 7th Ave</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33605</td>\n",
       "      <td>27.960766</td>\n",
       "      <td>-82.443715</td>\n",
       "      <td>4.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Alcohol': \"u'full_bar'\", 'RestaurantsReserva...</td>\n",
       "      <td>Latin American</td>\n",
       "      <td>{'Monday': '11:0-23:0', 'Tuesday': '11:0-23:0'...</td>\n",
       "      <td>State:Florida, City:Tampa, Address:1412 E 7th ...</td>\n",
       "      <td>Alcohol RestaurantsReservations BusinessAccept...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  business_id       name         address   city    state  \\\n",
       "43698  e4A1XPyEEMcfXxzDQhMrDQ  Flor Fina  1412 E 7th Ave  Tampa  Florida   \n",
       "43698  e4A1XPyEEMcfXxzDQhMrDQ  Flor Fina  1412 E 7th Ave  Tampa  Florida   \n",
       "\n",
       "      postal_code   latitude  longitude  stars  review_count  is_open  \\\n",
       "43698       33605  27.960766 -82.443715    4.0            72        1   \n",
       "43698       33605  27.960766 -82.443715    4.0            72        1   \n",
       "\n",
       "                                              attributes      categories  \\\n",
       "43698  {'Alcohol': \"u'full_bar'\", 'RestaurantsReserva...   Mediterranean   \n",
       "43698  {'Alcohol': \"u'full_bar'\", 'RestaurantsReserva...  Latin American   \n",
       "\n",
       "                                                   hours  \\\n",
       "43698  {'Monday': '11:0-23:0', 'Tuesday': '11:0-23:0'...   \n",
       "43698  {'Monday': '11:0-23:0', 'Tuesday': '11:0-23:0'...   \n",
       "\n",
       "                                                location  \\\n",
       "43698  State:Florida, City:Tampa, Address:1412 E 7th ...   \n",
       "43698  State:Florida, City:Tampa, Address:1412 E 7th ...   \n",
       "\n",
       "                                         attributes_true  \n",
       "43698  Alcohol RestaurantsReservations BusinessAccept...  \n",
       "43698  Alcohol RestaurantsReservations BusinessAccept...  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a random restaurant name\n",
    "random_name = df['name'].sample(n=1).values[0]\n",
    "print(\"Random Restaurant Name:\", random_name)\n",
    "\n",
    "# Information on sampled restaurant\n",
    "df[df.name==random_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random State: Idaho\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "      <th>stars</th>\n",
       "      <th>address</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>Café Vicino</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>4.5</td>\n",
       "      <td>808 W Fort St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>Tazah Kabob</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5755 W Overland Rd</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Kabob House</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9140 W Emerald St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>Rendezvous</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Eagle</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1117 E Winding Creek Dr</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>Aladdin Egyptian Cuisine</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>4.0</td>\n",
       "      <td>350 N Milwaukee St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Cazba</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>4.0</td>\n",
       "      <td>211 N 8th St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Le Coq d'Or</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Eagle</td>\n",
       "      <td>4.0</td>\n",
       "      <td>176 S Rosebud Ln</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Food Land Market</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>5.0</td>\n",
       "      <td>710 N Orchard St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Richard's</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>4.0</td>\n",
       "      <td>500 S Capitol Blvd, Ste 100</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>Bosnia Express Boex</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Boise</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4846 Emerald St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name  state   city  stars  \\\n",
       "661               Café Vicino  Idaho  Boise    4.5   \n",
       "702               Tazah Kabob  Idaho  Boise    5.0   \n",
       "42                Kabob House  Idaho  Boise    4.5   \n",
       "601                Rendezvous  Idaho  Eagle    4.5   \n",
       "517  Aladdin Egyptian Cuisine  Idaho  Boise    4.0   \n",
       "104                     Cazba  Idaho  Boise    4.0   \n",
       "99                Le Coq d'Or  Idaho  Eagle    4.0   \n",
       "295          Food Land Market  Idaho  Boise    5.0   \n",
       "56                  Richard's  Idaho  Boise    4.0   \n",
       "832       Bosnia Express Boex  Idaho  Boise    4.5   \n",
       "\n",
       "                         address     categories  \n",
       "661                808 W Fort St  Mediterranean  \n",
       "702           5755 W Overland Rd  Mediterranean  \n",
       "42             9140 W Emerald St  Mediterranean  \n",
       "601      1117 E Winding Creek Dr  Mediterranean  \n",
       "517           350 N Milwaukee St  Mediterranean  \n",
       "104                 211 N 8th St  Mediterranean  \n",
       "99              176 S Rosebud Ln  Mediterranean  \n",
       "295             710 N Orchard St  Mediterranean  \n",
       "56   500 S Capitol Blvd, Ste 100  Mediterranean  \n",
       "832              4846 Emerald St  Mediterranean  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Randomly chosen state\n",
    "random_state = df['state'].sample(n=1).values[0]\n",
    "print(\"Random State:\", random_state)\n",
    "\n",
    "# recommendations based on random state and random restaurant name\n",
    "restaurants = recommendation(df, state=random_state,  name=random_name)\n",
    "restaurants.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random State: Tennessee\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "      <th>stars</th>\n",
       "      <th>address</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3351</th>\n",
       "      <td>Margot Cafe &amp; Bar</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1017 Woodland St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789</th>\n",
       "      <td>Epice</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2902 12th Ave S</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>Sadie's</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1200 Villa Pl</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>North Italia</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4041 Aspen Grove Dr, Ste 100</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3000</th>\n",
       "      <td>Rolf and Daughters</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>4.5</td>\n",
       "      <td>700 Taylor St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>Le Skewer</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Brentwood</td>\n",
       "      <td>4.5</td>\n",
       "      <td>330 Franklin Rd</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Aladdin's Hookah Lounge and Bar</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2206 Elliston Pl</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2542</th>\n",
       "      <td>Aladdin's Hookah Lounge &amp; Bar</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2000 Belcourt Ave</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Inchin's Bamboo Garden</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1800 West End Ave</td>\n",
       "      <td>Chinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Greko Greek Street Food</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>4.0</td>\n",
       "      <td>704 Main St</td>\n",
       "      <td>Mediterranean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name      state       city  stars  \\\n",
       "3351                Margot Cafe & Bar  Tennessee  Nashville    4.5   \n",
       "2789                            Epice  Tennessee  Nashville    4.5   \n",
       "2132                          Sadie's  Tennessee  Nashville    4.5   \n",
       "171                      North Italia  Tennessee   Franklin    4.0   \n",
       "3000               Rolf and Daughters  Tennessee  Nashville    4.5   \n",
       "3129                        Le Skewer  Tennessee  Brentwood    4.5   \n",
       "194   Aladdin's Hookah Lounge and Bar  Tennessee  Nashville    3.5   \n",
       "2542    Aladdin's Hookah Lounge & Bar  Tennessee  Nashville    3.0   \n",
       "163            Inchin's Bamboo Garden  Tennessee  Nashville    4.0   \n",
       "113           Greko Greek Street Food  Tennessee  Nashville    4.0   \n",
       "\n",
       "                           address     categories  \n",
       "3351              1017 Woodland St  Mediterranean  \n",
       "2789               2902 12th Ave S  Mediterranean  \n",
       "2132                 1200 Villa Pl  Mediterranean  \n",
       "171   4041 Aspen Grove Dr, Ste 100        Italian  \n",
       "3000                 700 Taylor St  Mediterranean  \n",
       "3129               330 Franklin Rd  Mediterranean  \n",
       "194               2206 Elliston Pl  Mediterranean  \n",
       "2542             2000 Belcourt Ave  Mediterranean  \n",
       "163              1800 West End Ave        Chinese  \n",
       "113                    704 Main St  Mediterranean  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Randomly chosen state\n",
    "random_state = df['state'].sample(n=1).values[0]\n",
    "print(\"Random State:\", random_state)\n",
    "\n",
    "# recommendations based on random state and random restaurant name\n",
    "restaurants = recommendation(df, state=random_state,  name=random_name)\n",
    "restaurants.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "\n",
    "- A randomly sampled restaurant name and a randomly sampled state were chosen for demostrational purposes.\n",
    "- It can be noted that, recommendation locations are accurate in that the restaurant may not be from that state but recommendations are given for the state in question.\n",
    "- We can also see that majority of the cuisines/categories match the restaurant in question.\n",
    "- Other attributes_true features contribute to the recommendations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random State: California\n",
      "Random Cuisine: American (Traditional)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
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       "      <th>categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>Venus in Furs</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18 E Cota St</td>\n",
       "      <td>American (Traditional)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46020</th>\n",
       "      <td>Uncorked Wine Tasting and Kitchen</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>5.0</td>\n",
       "      <td>432 E Haley St</td>\n",
       "      <td>American (Traditional)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46828</th>\n",
       "      <td>Corner Tap Room</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1905 Cliff Dr</td>\n",
       "      <td>American (Traditional)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>Oliver's</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1198 Coast Village Rd</td>\n",
       "      <td>American (Traditional)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>Cold Spring Tavern</td>\n",
       "      <td>California</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5995 Stagecoach Rd</td>\n",
       "      <td>American (Traditional)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    name       state           city  stars  \\\n",
       "6633                       Venus in Furs  California  Santa Barbara    5.0   \n",
       "46020  Uncorked Wine Tasting and Kitchen  California  Santa Barbara    5.0   \n",
       "46828                    Corner Tap Room  California  Santa Barbara    5.0   \n",
       "2215                            Oliver's  California  Santa Barbara    4.5   \n",
       "2793                  Cold Spring Tavern  California  Santa Barbara    4.5   \n",
       "\n",
       "                     address              categories  \n",
       "6633            18 E Cota St  American (Traditional)  \n",
       "46020         432 E Haley St  American (Traditional)  \n",
       "46828          1905 Cliff Dr  American (Traditional)  \n",
       "2215   1198 Coast Village Rd  American (Traditional)  \n",
       "2793      5995 Stagecoach Rd  American (Traditional)  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_state = df['state'].sample(n=1).values[0]\n",
    "print(\"Random State:\", random_state)\n",
    "\n",
    "random_cuisine = df['categories'].sample(n=1).values[0]\n",
    "print(\"Random Cuisine:\", random_cuisine)\n",
    "\n",
    "# Example recommendations based on state, category/cuisine\n",
    "cuisines = recommendation(df, state=random_state,  category=random_cuisine)\n",
    "cuisines.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "***\n",
    "- A randomly sampled state and a randomly sampled cuisine was used for demostration puposes\n",
    "- It can be seen that the system recommends the cuisine desired \n",
    "- As well as recommending specifically in the state desired."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "##### 2. COLLABORATIVE FILTERING MODELS\n",
    "***\n",
    "\n",
    "Here the tasks related to building a collaborative filtering recommendation system using the Surprise library are undertaken for collaborative filtering by selecting the relevant columns, importing the Surprise library, initializing a Reader object to specify the data format, and then loading the data into a Surprise Dataset object for further analysis and model building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2559584 entries, 0 to 2559583\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Dtype \n",
      "---  ------       ----- \n",
      " 0   user_id      object\n",
      " 1   business_id  object\n",
      " 2   stars        int64 \n",
      " 3   date         object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 78.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Loading the users csv file\n",
    "users_data= pd.read_csv(\"data/cleaned_users_data.csv\")\n",
    "\n",
    "# summary information on data\n",
    "users_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>user_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>stars_x</th>\n",
       "      <th>date</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars_y</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "      <th>location</th>\n",
       "      <th>attributes_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bcjbaE6dDog4jkNY91ncLQ</td>\n",
       "      <td>e4Vwtrqf-wpJfwesgvdgxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>2017-01-14 20:54:15</td>\n",
       "      <td>Melt</td>\n",
       "      <td>2549 Banks St</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>70119</td>\n",
       "      <td>29.962102</td>\n",
       "      <td>-90.087958</td>\n",
       "      <td>4.0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>{'BusinessParking': \"{'garage': False, 'street...</td>\n",
       "      <td>American (Traditional)</td>\n",
       "      <td>{'Monday': '0:0-0:0', 'Friday': '11:0-17:0', '...</td>\n",
       "      <td>State:Louisiana, City:New Orleans, Address:254...</td>\n",
       "      <td>BusinessParkingBusinessParking GoodForMealGood...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RreNy--tOmXMl1en0wiBOg</td>\n",
       "      <td>cPepkJeRMtHapc_b2Oe_dw</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-07-17 03:30:07</td>\n",
       "      <td>Naked Tchopstix Express</td>\n",
       "      <td>2902 W 86th St, Ste 70</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>46268</td>\n",
       "      <td>39.912505</td>\n",
       "      <td>-86.211285</td>\n",
       "      <td>3.5</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>{'GoodForMeal': \"{'dessert': False, 'latenight...</td>\n",
       "      <td>Hawaiian</td>\n",
       "      <td>{'Monday': '11:0-21:0', 'Tuesday': '11:0-21:0'...</td>\n",
       "      <td>State:Indiana, City:Indianapolis, Address:2902...</td>\n",
       "      <td>OutdoorSeating RestaurantsTakeOut RestaurantsG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jha0USGDMefGFRLik_xFQg</td>\n",
       "      <td>bMratNjTG5ZFEA6hVyr-xQ</td>\n",
       "      <td>5</td>\n",
       "      <td>2017-02-19 13:32:05</td>\n",
       "      <td>Portobello Cafe</td>\n",
       "      <td>1423 Chester Pike</td>\n",
       "      <td>Eddystone</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19022</td>\n",
       "      <td>39.865032</td>\n",
       "      <td>-75.344051</td>\n",
       "      <td>4.0</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>{'BikeParking': 'True', 'RestaurantsReservatio...</td>\n",
       "      <td>Italian</td>\n",
       "      <td>{'Monday': '16:30-21:0', 'Tuesday': '16:30-21:...</td>\n",
       "      <td>State:Pennsylvania, City:Eddystone, Address:14...</td>\n",
       "      <td>BikeParking RestaurantsReservations HasTV Rest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iYY5Ii1LGpZCpXFkHlMefw</td>\n",
       "      <td>Zx7n8mdt8OzLRXVzolXNhQ</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-04-27 23:03:21</td>\n",
       "      <td>Milk and Honey Nashville</td>\n",
       "      <td>214 11th Ave S</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>37203</td>\n",
       "      <td>36.154702</td>\n",
       "      <td>-86.784541</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1725</td>\n",
       "      <td>1</td>\n",
       "      <td>{'WheelchairAccessible': 'True', 'RestaurantsP...</td>\n",
       "      <td>American (Traditional)</td>\n",
       "      <td>{'Monday': '0:0-0:0', 'Thursday': '6:30-15:0',...</td>\n",
       "      <td>State:Tennessee, City:Nashville, Address:214 1...</td>\n",
       "      <td>WheelchairAccessible RestaurantsPriceRange2 Bu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S7bjj-L07JuRr-tpX1UZLw</td>\n",
       "      <td>I6L0Zxi5Ww0zEWSAVgngeQ</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-07-07 20:50:12</td>\n",
       "      <td>Cafe Beignet on Bourbon Street</td>\n",
       "      <td>311 Bourbon St</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>70130</td>\n",
       "      <td>29.955845</td>\n",
       "      <td>-90.068436</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1066</td>\n",
       "      <td>1</td>\n",
       "      <td>{'GoodForKids': 'True', 'OutdoorSeating': 'Tru...</td>\n",
       "      <td>Cajun/Creole</td>\n",
       "      <td>{'Monday': '0:0-0:0', 'Tuesday': '8:0-15:0', '...</td>\n",
       "      <td>State:Louisiana, City:New Orleans, Address:311...</td>\n",
       "      <td>GoodForKids OutdoorSeating BusinessAcceptsCred...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  user_id             business_id  stars_x  \\\n",
       "0  bcjbaE6dDog4jkNY91ncLQ  e4Vwtrqf-wpJfwesgvdgxQ        4   \n",
       "1  RreNy--tOmXMl1en0wiBOg  cPepkJeRMtHapc_b2Oe_dw        4   \n",
       "2  Jha0USGDMefGFRLik_xFQg  bMratNjTG5ZFEA6hVyr-xQ        5   \n",
       "3  iYY5Ii1LGpZCpXFkHlMefw  Zx7n8mdt8OzLRXVzolXNhQ        5   \n",
       "4  S7bjj-L07JuRr-tpX1UZLw  I6L0Zxi5Ww0zEWSAVgngeQ        4   \n",
       "\n",
       "                  date                            name  \\\n",
       "0  2017-01-14 20:54:15                            Melt   \n",
       "1  2018-07-17 03:30:07         Naked Tchopstix Express   \n",
       "2  2017-02-19 13:32:05                 Portobello Cafe   \n",
       "3  2018-04-27 23:03:21        Milk and Honey Nashville   \n",
       "4  2018-07-07 20:50:12  Cafe Beignet on Bourbon Street   \n",
       "\n",
       "                  address          city         state postal_code   latitude  \\\n",
       "0           2549 Banks St   New Orleans     Louisiana       70119  29.962102   \n",
       "1  2902 W 86th St, Ste 70  Indianapolis       Indiana       46268  39.912505   \n",
       "2       1423 Chester Pike     Eddystone  Pennsylvania       19022  39.865032   \n",
       "3          214 11th Ave S     Nashville     Tennessee       37203  36.154702   \n",
       "4          311 Bourbon St   New Orleans     Louisiana       70130  29.955845   \n",
       "\n",
       "   longitude  stars_y  review_count  is_open  \\\n",
       "0 -90.087958      4.0            32        0   \n",
       "1 -86.211285      3.5            33        0   \n",
       "2 -75.344051      4.0           137        1   \n",
       "3 -86.784541      4.0          1725        1   \n",
       "4 -90.068436      3.5          1066        1   \n",
       "\n",
       "                                          attributes              categories  \\\n",
       "0  {'BusinessParking': \"{'garage': False, 'street...  American (Traditional)   \n",
       "1  {'GoodForMeal': \"{'dessert': False, 'latenight...                Hawaiian   \n",
       "2  {'BikeParking': 'True', 'RestaurantsReservatio...                 Italian   \n",
       "3  {'WheelchairAccessible': 'True', 'RestaurantsP...  American (Traditional)   \n",
       "4  {'GoodForKids': 'True', 'OutdoorSeating': 'Tru...            Cajun/Creole   \n",
       "\n",
       "                                               hours  \\\n",
       "0  {'Monday': '0:0-0:0', 'Friday': '11:0-17:0', '...   \n",
       "1  {'Monday': '11:0-21:0', 'Tuesday': '11:0-21:0'...   \n",
       "2  {'Monday': '16:30-21:0', 'Tuesday': '16:30-21:...   \n",
       "3  {'Monday': '0:0-0:0', 'Thursday': '6:30-15:0',...   \n",
       "4  {'Monday': '0:0-0:0', 'Tuesday': '8:0-15:0', '...   \n",
       "\n",
       "                                            location  \\\n",
       "0  State:Louisiana, City:New Orleans, Address:254...   \n",
       "1  State:Indiana, City:Indianapolis, Address:2902...   \n",
       "2  State:Pennsylvania, City:Eddystone, Address:14...   \n",
       "3  State:Tennessee, City:Nashville, Address:214 1...   \n",
       "4  State:Louisiana, City:New Orleans, Address:311...   \n",
       "\n",
       "                                     attributes_true  \n",
       "0  BusinessParkingBusinessParking GoodForMealGood...  \n",
       "1  OutdoorSeating RestaurantsTakeOut RestaurantsG...  \n",
       "2  BikeParking RestaurantsReservations HasTV Rest...  \n",
       "3  WheelchairAccessible RestaurantsPriceRange2 Bu...  \n",
       "4  GoodForKids OutdoorSeating BusinessAcceptsCred...  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging the two datasets into one using the business_id primary key\n",
    "data=pd.merge(left=users_data, right=df, how='inner', on='business_id')\n",
    "\n",
    "# previewing the new merge dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2386717 entries, 0 to 2386716\n",
      "Data columns (total 19 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   user_id          object \n",
      " 1   business_id      object \n",
      " 2   b/s_rating       int64  \n",
      " 3   date             object \n",
      " 4   name             object \n",
      " 5   address          object \n",
      " 6   city             object \n",
      " 7   state            object \n",
      " 8   postal_code      object \n",
      " 9   latitude         float64\n",
      " 10  longitude        float64\n",
      " 11  rating           float64\n",
      " 12  review_count     int64  \n",
      " 13  is_open          int64  \n",
      " 14  attributes       object \n",
      " 15  categories       object \n",
      " 16  hours            object \n",
      " 17  location         object \n",
      " 18  attributes_true  object \n",
      "dtypes: float64(3), int64(3), object(13)\n",
      "memory usage: 346.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Renaming the **stars_x** and **stars_y** columns into **rating** and **b/s_rating** columns for better understanding\n",
    "data.rename(columns={'stars_x':'b/s_rating', 'stars_y':'rating'}, inplace=True)\n",
    "\n",
    "# previewing the data information\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Preprocessing Data For Modeling**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of users:  738495 \n",
      "\n",
      "Number of Restaurants:  24835\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#selecting specific columns that are relevant for collaborative filtering models\n",
    "new_df = data[['user_id', 'business_id', 'rating']]\n",
    "\n",
    "# using Reader() from surprise module to convert dataframe into surprise dataformat\n",
    "# instantiating a reader object\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "\n",
    "# using the reader to read the trainset\n",
    "data_2 = Dataset.load_from_df(new_df,reader)\n",
    "\n",
    "# Creating a train set with all available data\n",
    "dataset = data_2.build_full_trainset()\n",
    "\n",
    "# Split the data into training and test sets\n",
    "trainset, testset = train_test_split(data_2, test_size=0.25)\n",
    "\n",
    "print('Number of users: ', dataset.n_users, '\\n')\n",
    "print('Number of Restaurants: ', dataset.n_items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the data as a pickle\n",
    "\n",
    "with open('pickled_files/new_df.pkl','wb') as file:\n",
    "    pickle.dump(new_df, file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Baseline Model Using Normal Predictor**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.8185\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8185080955430073"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Normal Predictor algorithm\n",
    "model_1 = NormalPredictor()\n",
    "\n",
    "# Train the model on the training set\n",
    "model_1.fit(trainset)\n",
    "\n",
    "# Predict ratings for the test set\n",
    "predictions = model_1.test(testset)\n",
    "\n",
    "# Compute rmse\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***\n",
    "- A normal predictor model from the surpise library was used as the initial dummy prediction model\n",
    "- The model was able to achieve an RMSE of 0.82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **NMF Model With Default Parameters**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.3492\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.34923950933089953"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the NMF algorithm\n",
    "model_2 = NMF(random_state=42)\n",
    "\n",
    "# Train the model on the training set\n",
    "model_2.fit(trainset)\n",
    "\n",
    "# Predict ratings for the test set\n",
    "predictions = model_2.test(testset)\n",
    "\n",
    "# Compute RMSE\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***\n",
    "- A Non-Negative Matrix Factorization(NMF) model was used as it is ideal when ratings are non-negative (i.e., ratings from 1 to 5).\n",
    "- The model was able to achieve an RMSE of 0.3492 which was a great improvement on the Normal Predictor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **SVD Model With Default Parameters**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.1177\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.11767655205211427"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the SVD algorithm\n",
    "model_3 = SVD(random_state=42)\n",
    "\n",
    "# Train the model on the training set\n",
    "model_3.fit(trainset)\n",
    "\n",
    "# Predict ratings for the test set\n",
    "predictions = model_3.test(testset)\n",
    "\n",
    "# Compute RMSE\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('test_rmse', array([0.11421767, 0.11203492, 0.11429362, 0.11313845, 0.11369016]))\n",
      "('test_mae', array([0.0543723 , 0.05407876, 0.05449346, 0.05422711, 0.05422642]))\n",
      "('fit_time', (58.86489272117615, 59.03896999359131, 56.809136390686035, 52.078330278396606, 57.26115536689758))\n",
      "('test_time', (11.661788702011108, 10.65920901298523, 9.922400951385498, 16.127597093582153, 17.293368816375732))\n",
      "-------------------------\n",
      "Mean RMSE:  0.11347496418141394\n"
     ]
    }
   ],
   "source": [
    "# using cross-validate to get the test rmse scores for 5 splits\n",
    "results=cross_validate(model_3, data_2, cv=5, n_jobs=-1)\n",
    "\n",
    "\n",
    "for values in results.items():\n",
    "    print(values)\n",
    "print(\"-------------------------\")\n",
    "print(\"Mean RMSE: \",results['test_rmse'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***\n",
    "- A Singe value Decomposition(SVD) model was used as it works well with explicit feedback (i.e. ratings)\n",
    "- The model was able to achieve an RMSE of 0.117 which further improved the RMSE\n",
    "- The model was then cross validated and ahcieved an RMSE mean of 0.113"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Hyperparameter Tuning SVD model**\n",
    "***\n",
    "Hyperparameter tuning was carried out using grid search and cross-validation. It tests different values of the number of latent factors (n_factors),regularization term (reg_all) and the number of epochs (n_epochs) to find the combination that results in the best model performance. The final best hyperparameters can be accessed from the g_s_svd object for use in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a dictionary params with hyperparameter values to be tested\n",
    "params = {'n_factors': [20, 50, 100], \n",
    "         'reg_all': [00.01, .02, 0.05],\n",
    "         'n_epochs':[20,30,40]} \n",
    "\n",
    "# create a GridSearchCV object 'g_s_svd' for hyperparameter tuning\n",
    "g_s_svd = GridSearchCV(SVD,param_grid=params,n_jobs=-1) \n",
    "\n",
    "# fit the GridSearchCV object to the data to find the best hyperparameters\n",
    "g_s_svd.fit(data_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'rmse': 0.06569255672542271, 'mae': 0.02687813158008088}\n",
      "{'rmse': {'n_factors': 20, 'reg_all': 0.01, 'n_epochs': 40}, 'mae': {'n_factors': 20, 'reg_all': 0.01, 'n_epochs': 40}}\n"
     ]
    }
   ],
   "source": [
    "print(g_s_svd.best_score)\n",
    "print(g_s_svd.best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.0684\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.06842456225531673"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting optimal parameters\n",
    "\n",
    "# Initialize the SVD algorithm\n",
    "model_4 = SVD(n_factors= 20, reg_all=0.01, n_epochs = 40,random_state=0)\n",
    "\n",
    "# Train the model on the training set\n",
    "model_4.fit(trainset)\n",
    "\n",
    "# Predict ratings for the test set\n",
    "predictions = model_4.test(testset)\n",
    "\n",
    "# Compute RMSE\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('test_rmse', array([0.06577581, 0.0645017 , 0.06589523, 0.06686514, 0.06527982]))\n",
      "('test_mae', array([0.02677181, 0.02681044, 0.02675453, 0.02696926, 0.02687886]))\n",
      "('fit_time', (48.65043520927429, 44.267767667770386, 41.27116274833679, 40.543596506118774, 37.63088870048523))\n",
      "('test_time', (8.27979850769043, 8.373634576797485, 8.25153660774231, 7.737122535705566, 9.878244876861572))\n",
      "-------------------------\n",
      "Mean RMSE:  0.06566353751416934\n"
     ]
    }
   ],
   "source": [
    "# using cross-validate to get the test rmse scores for 5 splits\n",
    "results=cross_validate(model_4, data_2, cv=5, n_jobs=-1)\n",
    "\n",
    "\n",
    "for values in results.items():\n",
    "    print(values)\n",
    "print(\"-------------------------\")\n",
    "print(\"Mean RMSE: \",results['test_rmse'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the model into a pickle\n",
    "with open('pickled_files/svd.pkl', 'wb') as file:\n",
    "    pickle.dump(model_4, file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***\n",
    "\n",
    "The RMSE value for the optimized SVD model is approximately 0.07, indicating the model's average prediction error in terms of user ratings. Lower RMSE values are desirable as they signify better predictive accuracy.                              \n",
    "                                           \n",
    "The best-performing hyperparameter values are as follows:                       \n",
    "For optimal RMSE, the optimal hyperparameters are 'n_factors' = 20,'reg_all' = 0.01 and 'n_epochs': 40.\n",
    "  \n",
    "These results indicate that the SVD collaborative filtering model, when configured with these hyperparameters, provides a relatively low prediction error and is well-suited for making personalized recommendations based on user ratings.\n",
    "\n",
    "This model was then saved in a pickle file for deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Collaborative filtering Intergration**\n",
    "***\n",
    "\n",
    "\n",
    "This below  allows a user to interactively rate restaurants by providing their ratings for a specified number of restaurants, and it collects this information in a list for further analysis or use in a recommendation system. The code also considers the restaurant category for selecting restaurants to rate if a category is provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_ratings(df, state, num_samples=5, max_ratings=3):\n",
    "    \"\"\"\n",
    "    Function to collect ratings for a number of randomly sampled restaurants.\n",
    "    Allows users to skip restaurants they have never been to and limits the total number of final ratings to a specified maximum.\n",
    "    \"\"\"\n",
    "    ratings = []\n",
    "\n",
    "    while len(ratings) < max_ratings:\n",
    "        # Sampling the specified number of restaurants\n",
    "        filtered_df=df[df.state==state]\n",
    "\n",
    "        sampled_restaurants = filtered_df.sample(n=num_samples)\n",
    "        \n",
    "        for _, row in sampled_restaurants.iterrows():\n",
    "            restaurant_id = row['business_id']\n",
    "            restaurant_name = row['name']\n",
    "            restaurant_state=row[\"state\"]\n",
    "            restaurant_city =row[\"city\"]\n",
    "\n",
    "            while True:\n",
    "                # Ask if the user has been to the restaurant\n",
    "                been_to_restaurant = input(f\"Have you been to {restaurant_name}, {restaurant_state},{restaurant_city}? (y/n): \").strip().lower()\n",
    "                \n",
    "                if been_to_restaurant == 'n':\n",
    "                    print(f\"Skipping {restaurant_name}.\")\n",
    "                    break  \n",
    "                \n",
    "                elif been_to_restaurant == 'y':\n",
    "                    while True:\n",
    "                        try:\n",
    "                            # Prompt user for rating\n",
    "                            print(f\"Please rate {restaurant_name} on a scale of 1 to 5:\")\n",
    "                            rating = int(input())\n",
    "                            \n",
    "                            # Check if the rating is within the valid range\n",
    "                            if 1 <= rating <= 5:\n",
    "                                print(f\"Rating for {restaurant_name}: {rating}\")\n",
    "                                ratings.append((restaurant_id, rating))\n",
    "                                \n",
    "                                # Check if we have reached the maximum number of ratings\n",
    "                                if len(ratings) >= max_ratings:\n",
    "                                    print(\"Maximum number of ratings collected.\")\n",
    "                                    return ratings\n",
    "                                \n",
    "                                break  \n",
    "                            else:\n",
    "                                print(\"Rating must be between 1 and 5. Please try again.\")\n",
    "                        except ValueError:\n",
    "                            print(\"Invalid input. Please enter a number between 1 and 5.\")\n",
    "                    break  \n",
    "                \n",
    "                else:\n",
    "                    print(\"Please answer 'yes' or 'no'.\")\n",
    "                \n",
    "        # If not enough ratings are collected, continue sampling\n",
    "        print(f\"Collected {len(ratings)} ratings so far. Collecting more samples.\")\n",
    "\n",
    "    return ratings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_restaurants(user_id, rated_restaurants, all_restaurants_df=df, state=None):\n",
    "\n",
    "    \"\"\"\n",
    "    Function to recommend restaurants based on state. the rated restaurnats are concatenated to the new_df dataframe, a model is created\n",
    "    the rated restaurants are filtered from the unrated restaurants and prediction is performed. the resulting dataframe is sorted by highest \n",
    "    predicted ratings \n",
    "    \"\"\"\n",
    "    # Filter by state if provided\n",
    "    all_restaurants_df = all_restaurants_df[all_restaurants_df[\"state\"] == state]\n",
    "\n",
    "    # Get all restaurant IDs\n",
    "    all_restaurant_ids = all_restaurants_df['business_id'].unique()\n",
    "\n",
    "    # Prepare the data for training the model\n",
    "    # Create DataFrame for the user's ratings\n",
    "    user_ratings_df = pd.DataFrame(rated_restaurants, columns=['business_id', 'rating'])\n",
    "    user_ratings_df['user_id'] = user_id  \n",
    "\n",
    "    # Combine user-specific ratings with the full dataset\n",
    "    combined_df = pd.concat([new_df, user_ratings_df])\n",
    "\n",
    "    # Filter combined_df to include only relevant restaurants\n",
    "    filtered_df = combined_df[combined_df['business_id'].isin(all_restaurant_ids)]\n",
    "\n",
    "    # Define the Reader and Dataset\n",
    "    reader = Reader(rating_scale=(1, 5))\n",
    "    data = Dataset.load_from_df(filtered_df[['user_id', 'business_id', 'rating']], reader)\n",
    "\n",
    "    # trainset, testset = train_test_split(data, test_size=0.2, random_state=42)\n",
    "    trainset = data.build_full_trainset()\n",
    "\n",
    "    # Retrain the model\n",
    "    model_4 = SVD(n_factors= 20, reg_all=0.01, n_epochs = 40, random_state=42)\n",
    "    model_4.fit(trainset)\n",
    "\n",
    "    # Filter out the restaurants that the user has already rated\n",
    "    rated_restaurant_ids = [rid for rid, _ in rated_restaurants]\n",
    "    unrated_restaurants = [rid for rid in all_restaurant_ids if rid not in rated_restaurant_ids]\n",
    "\n",
    "    # Predict ratings for all unrated restaurants\n",
    "    predictions = [model_4.predict(user_id, rid) for rid in unrated_restaurants]\n",
    "\n",
    "    # Create a DataFrame for the predictions\n",
    "    pred_df = pd.DataFrame({\n",
    "        'business_id': [pred.iid for pred in predictions],\n",
    "        'predicted_rating': [pred.est for pred in predictions]\n",
    "    })\n",
    "\n",
    "    # Merge with the original restaurants DataFrame to get more information\n",
    "    recommendations = pred_df.merge(all_restaurants_df, on='business_id', how='left')\n",
    "\n",
    "    # Sort by predicted rating and get top recommendations\n",
    "    recommendations = recommendations.sort_values(by='predicted_rating', ascending=False)\n",
    "\n",
    "    return recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You will be asked to rate 5 random restaurants.\n",
      "Skipping Tilted Kilt Pub & Eatery.\n",
      "Please rate Munk & Nunn on a scale of 1 to 5:\n",
      "Rating for Munk & Nunn: 3\n",
      "Skipping Sandman's Hookah Lounge.\n",
      "Please rate Mamoun's Falafel - Philadelphia, PA on a scale of 1 to 5:\n",
      "Rating for Mamoun's Falafel - Philadelphia, PA: 5\n",
      "Please rate Swiss Haus Cafe & Pastry Bar on a scale of 1 to 5:\n",
      "Rating for Swiss Haus Cafe & Pastry Bar: 5\n",
      "Maximum number of ratings collected.\n",
      "[('OaTNoQOXH6JW4lK9pITYGg', 3), ('AfM_dYO_LTqQz33qHbT45A', 5), ('vCHNWdW-ys-nWUx3Cpvk8Q', 5)]\n"
     ]
    }
   ],
   "source": [
    "# Collect ratings from the user\n",
    "print(\"You will be asked to rate 5 random restaurants.\")\n",
    "user_ratings = collect_ratings(df, state=\"Pennsylvania\")\n",
    "print(user_ratings)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>business_id</th>\n",
       "      <th>predicted_rating</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>stars</th>\n",
       "      <th>review_count</th>\n",
       "      <th>is_open</th>\n",
       "      <th>attributes</th>\n",
       "      <th>categories</th>\n",
       "      <th>hours</th>\n",
       "      <th>location</th>\n",
       "      <th>attributes_true</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>qQGoc6H4l1YqEem0sBwFHQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Poke Bros</td>\n",
       "      <td>124 E Market St, Unit B</td>\n",
       "      <td>West Chester</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19382</td>\n",
       "      <td>39.960352</td>\n",
       "      <td>-75.602836</td>\n",
       "      <td>5.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsTakeOut': 'True', 'Ambience': \"{'...</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>{'Monday': '0:0-0:0', 'Tuesday': '11:0-21:0', ...</td>\n",
       "      <td>State:Pennsylvania, City:West Chester, Address...</td>\n",
       "      <td>RestaurantsTakeOut RestaurantsDelivery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>YA4OQvZFowHG4_78xis7sg</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Boricua Restaurant</td>\n",
       "      <td>1149 N 3rd St</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19123</td>\n",
       "      <td>39.968903</td>\n",
       "      <td>-75.141464</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>{'BusinessParking': \"{'garage': False, 'street...</td>\n",
       "      <td>Puerto Rican</td>\n",
       "      <td>{'Monday': '11:0-19:30', 'Tuesday': '11:0-19:3...</td>\n",
       "      <td>State:Pennsylvania, City:Philadelphia, Address...</td>\n",
       "      <td>BusinessParkingBusinessParking Caters Alcohol ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7816</th>\n",
       "      <td>k3CN4-QImUeSTWpS1Qd6IQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Karla's Kitchen</td>\n",
       "      <td>1666 Bethlehem Pike</td>\n",
       "      <td>Flourtown</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19031</td>\n",
       "      <td>40.107447</td>\n",
       "      <td>-75.212677</td>\n",
       "      <td>5.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>{'RestaurantsDelivery': 'False', 'RestaurantsP...</td>\n",
       "      <td>Latin American</td>\n",
       "      <td>{'Monday': '11:0-19:0', 'Tuesday': '10:0-19:0'...</td>\n",
       "      <td>State:Pennsylvania, City:Flourtown, Address:16...</td>\n",
       "      <td>RestaurantsPriceRange2 WiFi RestaurantsGoodFor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7850</th>\n",
       "      <td>prWF6l3ukkw0zLl3tl-V-g</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Pupuseria Sabor Latino II</td>\n",
       "      <td>1446 E Hunting Park Ave</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19124</td>\n",
       "      <td>40.010016</td>\n",
       "      <td>-75.098324</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>{'OutdoorSeating': 'True', 'BusinessAcceptsCre...</td>\n",
       "      <td>Latin American</td>\n",
       "      <td>{'Monday': '9:0-20:0', 'Tuesday': '9:0-20:0', ...</td>\n",
       "      <td>State:Pennsylvania, City:Philadelphia, Address...</td>\n",
       "      <td>OutdoorSeating BusinessAcceptsCreditCards Rest...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7886</th>\n",
       "      <td>FX9CL_-hBxRDq6c4uiLCrQ</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Napoletano Brothers</td>\n",
       "      <td>186 Saxer Ave</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19064</td>\n",
       "      <td>39.927174</td>\n",
       "      <td>-75.333539</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>{'BusinessParking': \"{'garage': False, 'street...</td>\n",
       "      <td>Italian</td>\n",
       "      <td>{'Monday': '7:0-18:0', 'Tuesday': '7:0-18:0', ...</td>\n",
       "      <td>State:Pennsylvania, City:Springfield, Address:...</td>\n",
       "      <td>BusinessParkingBusinessParking Alcohol Restaur...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 business_id  predicted_rating                       name  \\\n",
       "1058  qQGoc6H4l1YqEem0sBwFHQ               5.0                  Poke Bros   \n",
       "555   YA4OQvZFowHG4_78xis7sg               5.0         Boricua Restaurant   \n",
       "7816  k3CN4-QImUeSTWpS1Qd6IQ               5.0            Karla's Kitchen   \n",
       "7850  prWF6l3ukkw0zLl3tl-V-g               5.0  Pupuseria Sabor Latino II   \n",
       "7886  FX9CL_-hBxRDq6c4uiLCrQ               5.0        Napoletano Brothers   \n",
       "\n",
       "                      address          city         state postal_code  \\\n",
       "1058  124 E Market St, Unit B  West Chester  Pennsylvania       19382   \n",
       "555             1149 N 3rd St  Philadelphia  Pennsylvania       19123   \n",
       "7816      1666 Bethlehem Pike     Flourtown  Pennsylvania       19031   \n",
       "7850  1446 E Hunting Park Ave  Philadelphia  Pennsylvania       19124   \n",
       "7886            186 Saxer Ave   Springfield  Pennsylvania       19064   \n",
       "\n",
       "       latitude  longitude  stars  review_count  is_open  \\\n",
       "1058  39.960352 -75.602836    5.0            36        1   \n",
       "555   39.968903 -75.141464    5.0            31        1   \n",
       "7816  40.107447 -75.212677    5.0            23        1   \n",
       "7850  40.010016 -75.098324    5.0            14        1   \n",
       "7886  39.927174 -75.333539    5.0            30        1   \n",
       "\n",
       "                                             attributes      categories  \\\n",
       "1058  {'RestaurantsTakeOut': 'True', 'Ambience': \"{'...        Japanese   \n",
       "555   {'BusinessParking': \"{'garage': False, 'street...    Puerto Rican   \n",
       "7816  {'RestaurantsDelivery': 'False', 'RestaurantsP...  Latin American   \n",
       "7850  {'OutdoorSeating': 'True', 'BusinessAcceptsCre...  Latin American   \n",
       "7886  {'BusinessParking': \"{'garage': False, 'street...         Italian   \n",
       "\n",
       "                                                  hours  \\\n",
       "1058  {'Monday': '0:0-0:0', 'Tuesday': '11:0-21:0', ...   \n",
       "555   {'Monday': '11:0-19:30', 'Tuesday': '11:0-19:3...   \n",
       "7816  {'Monday': '11:0-19:0', 'Tuesday': '10:0-19:0'...   \n",
       "7850  {'Monday': '9:0-20:0', 'Tuesday': '9:0-20:0', ...   \n",
       "7886  {'Monday': '7:0-18:0', 'Tuesday': '7:0-18:0', ...   \n",
       "\n",
       "                                               location  \\\n",
       "1058  State:Pennsylvania, City:West Chester, Address...   \n",
       "555   State:Pennsylvania, City:Philadelphia, Address...   \n",
       "7816  State:Pennsylvania, City:Flourtown, Address:16...   \n",
       "7850  State:Pennsylvania, City:Philadelphia, Address...   \n",
       "7886  State:Pennsylvania, City:Springfield, Address:...   \n",
       "\n",
       "                                        attributes_true  \n",
       "1058             RestaurantsTakeOut RestaurantsDelivery  \n",
       "555   BusinessParkingBusinessParking Caters Alcohol ...  \n",
       "7816  RestaurantsPriceRange2 WiFi RestaurantsGoodFor...  \n",
       "7850  OutdoorSeating BusinessAcceptsCreditCards Rest...  \n",
       "7886  BusinessParkingBusinessParking Alcohol Restaur...  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entering user_id\n",
    "user_id = 'uu1651'\n",
    "\n",
    "# Recommendation based on state and ratings\n",
    "recommended_restaurants = recommend_restaurants(user_id=user_id, rated_restaurants=user_ratings, state= \"Pennsylvania\").drop_duplicates(subset='name')\n",
    "\n",
    "# Viewing the top 5 entries\n",
    "recommended_restaurants.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For the implementaion of collaborative filtering to be effective, it is highly dependent on the state from which you would choose to rate th restaurants. This is shown using the example above\n",
    "\n",
    "If you are from Pennsylvania state, Philadelphia city, you would receive rating requests for the state of Pennsylvania. It is possible to skip to only rate restaurants in Philadelphia city only.\n",
    "\n",
    "The user would have to rate at least 3 restaurants to be able to get recommendations\n",
    "\n",
    "These ratings would then be passed through an SVD model to predict all the unrated restaurants giving a predicted rating\n",
    "\n",
    "This predicted rating is then sorted in descending order and used to give a list of all recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "##### 3. NEURAL NETWORKS MODEL\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will run a Keras deep neural network to implement a recommendation system and try to improve our RMSE scores by using neural networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We are going to encode the user_id and business_id features into numeric integers in preparation for the deep learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Users:  220872\n",
      "Number of Restaurants:  31834\n"
     ]
    }
   ],
   "source": [
    "# Encoding the user_id column\n",
    "user_encoder = LabelEncoder()                                    # instantiating the encoder\n",
    "data['userId'] = user_encoder.fit_transform(data.user_id.values) # fitting and transforming the encoder to our column\n",
    "n_users=data['userId'].nunique()                                 # assigning the number of users to n_user vaiable\n",
    "print(\"Number of Users: \",n_users)\n",
    "\n",
    "# Encoding the business_id column\n",
    "item_encoder = LabelEncoder()                                          # instantiating the encoder\n",
    "data['restId'] = user_encoder.fit_transform(data.business_id.values)   # fitting and transforming the encoder to our column\n",
    "n_rests = data['restId'].nunique()                                  # assigning the number of restaurants to n_rests vaiable\n",
    "print(\"Number of Restaurants: \",n_rests)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Splitting the data into training and testing sets for model evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(428157, 2) (428157,)\n",
      "(107040, 2) (107040,)\n"
     ]
    }
   ],
   "source": [
    "# subsetting the x variable\n",
    "X = data[['userId', 'restId']].values\n",
    "# subsetting the y variable\n",
    "y = data['rating'].values\n",
    "\n",
    "# creating the train test splits and stratifying on basis of the y values \n",
    "# because of the uneven nature of the rating counts\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Calculate the minimum and maximum ratings, which will be used to scale the output of the neural network later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the minimum and maximum rating\n",
    "min_rating = min(data['rating'])\n",
    "max_rating = max(data['rating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The predicted ratings is calculated by multiplying the user and restaurant embeddings, then adding the user and restaurant bias. Therefore were are going to create user and restaurant embeddings together with bias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of latent factors\n",
    "embedding_size = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Defining user embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User embeddings\n",
    "\n",
    "# user input layer\n",
    "user = layers.Input(shape=(1,))\n",
    "\n",
    "# Embedding layer for calculating user latent factors of size 50\n",
    "user_emb = layers.Embedding(n_users, embedding_size, embeddings_regularizer=regularizers.l2(1e-6))(user)\n",
    "\n",
    "# Reshaping the layer to flatten the embedding vector.\n",
    "user_emb = layers.Reshape((embedding_size,))(user_emb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Defining user bias, and reshape it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# User bias\n",
    "\n",
    "# Embedding layer\n",
    "user_bias = layers.Embedding(n_users, 1, embeddings_regularizer=regularizers.l2(1e-6))(user)\n",
    "\n",
    "# Reshapin the user bias layer\n",
    "user_bias = layers.Reshape((1,))(user_bias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Defining restaurants embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# restaurant embeddings\n",
    "\n",
    "# Input layer\n",
    "restaurant= layers.Input(shape=(1,))\n",
    "\n",
    "# Embedding layer\n",
    "rest_emb = layers.Embedding(n_rests, embedding_size, embeddings_regularizer=regularizers.l2(1e-6))(restaurant)\n",
    "\n",
    "# Reshape layer\n",
    "rest_emb = layers.Reshape((embedding_size,))(rest_emb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Defining restaurant bias, and reshape it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Restaurant bias\n",
    "\n",
    "# Embedding layer\n",
    "rest_bias = layers.Embedding(n_rests, 1, embeddings_regularizer=regularizers.l2(1e-6))(restaurant)\n",
    "\n",
    "# Reshape layer\n",
    "rest_bias = layers.Reshape((1,))(rest_bias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> After defining the embedding and bias layers, the predicted rating is calculated by dot product of the user and restaurant embeddings and then adding the bias values in order to get more accurate ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dot product of the user and restaurant embeddings\n",
    "rating = layers.Concatenate()([user_emb, rest_emb])\n",
    "\n",
    "# Add biases to the ratings\n",
    "# Adding the user and restaurant bias to the predicted rating\n",
    "rating = layers.Add()([rating, user_bias, rest_bias])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We move on to pass the calculated rating to layers of dense networks and finally converting the rating score from binary values into a range of 1-5. \n",
    "\n",
    "We create our baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m246s\u001b[0m 146ms/step - loss: 2.2653 - root_mean_squared_error: 1.5015 - val_loss: 2.2630 - val_root_mean_squared_error: 1.5007\n",
      "Epoch 2/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m248s\u001b[0m 148ms/step - loss: 2.2557 - root_mean_squared_error: 1.4983 - val_loss: 2.2616 - val_root_mean_squared_error: 1.5003\n",
      "Epoch 3/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 138ms/step - loss: 2.2602 - root_mean_squared_error: 1.4998 - val_loss: 2.2623 - val_root_mean_squared_error: 1.5005\n",
      "Epoch 4/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m341s\u001b[0m 185ms/step - loss: 2.2639 - root_mean_squared_error: 1.5011 - val_loss: 2.2614 - val_root_mean_squared_error: 1.5002\n",
      "Epoch 5/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m275s\u001b[0m 157ms/step - loss: 2.2591 - root_mean_squared_error: 1.4994 - val_loss: 2.2607 - val_root_mean_squared_error: 1.5000\n",
      "Epoch 6/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m306s\u001b[0m 147ms/step - loss: 2.2514 - root_mean_squared_error: 1.4969 - val_loss: 2.2590 - val_root_mean_squared_error: 1.4994\n",
      "Epoch 7/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 140ms/step - loss: 2.2587 - root_mean_squared_error: 1.4993 - val_loss: 2.2549 - val_root_mean_squared_error: 1.4980\n",
      "Epoch 8/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m275s\u001b[0m 148ms/step - loss: 2.2457 - root_mean_squared_error: 1.4950 - val_loss: 2.2322 - val_root_mean_squared_error: 1.4905\n",
      "Epoch 9/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m315s\u001b[0m 180ms/step - loss: 2.1951 - root_mean_squared_error: 1.4780 - val_loss: 2.1834 - val_root_mean_squared_error: 1.4740\n",
      "Epoch 10/10\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m334s\u001b[0m 187ms/step - loss: 2.0870 - root_mean_squared_error: 1.4409 - val_loss: 2.1166 - val_root_mean_squared_error: 1.4512\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7f21fe2e3950>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# first dense layer of 30 nodes with relu activation\n",
    "rating = layers.Dense(30, activation='relu')(rating)\n",
    "\n",
    "# second dense layer of 15 nodes\n",
    "rating = layers.Dense(15, activation='relu')(rating)\n",
    "\n",
    "# output layer with one node that produces values between 0 and 1 due to the sigmoid activation\n",
    "rating = layers.Dense(1, activation='sigmoid')(rating)\n",
    "# rating= layers.Dense(5, activation='softmax')(rating)\n",
    "\n",
    "# Scales the predicted ratings to a range of 1 - 5\n",
    "rating = layers.Lambda(lambda x:x*(max_rating - min_rating) + min_rating)(rating)\n",
    "\n",
    "\n",
    "# Baseline Model \n",
    "baseline_model = models.Model([user, restaurant], rating)\n",
    "\n",
    "# Compile the model\n",
    "baseline_model.compile( optimizer='sgd', loss='mse',  metrics=[metrics.RootMeanSquaredError()])\n",
    "\n",
    "# training the model\n",
    "baseline_model .fit(x=[X_train[:,0], X_train[:,1]], y=y_train,\n",
    "                    batch_size=256, \n",
    "                    epochs=10, \n",
    "                    verbose=1,\n",
    "                    validation_data=([X_test[:,0], X_test[:,1]], y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Our baseline model, does not overfit since the training RMSE score and the validation scores are not far off. We then proceed to tune the model in order to get better rmse scores, by reducing the model complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m313s\u001b[0m 184ms/step - loss: 2.1169 - root_mean_squared_error: 1.4413 - val_loss: 2.0627 - val_root_mean_squared_error: 1.4232\n",
      "Epoch 2/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m256s\u001b[0m 153ms/step - loss: 1.8676 - root_mean_squared_error: 1.3530 - val_loss: 2.0022 - val_root_mean_squared_error: 1.4022\n",
      "Epoch 3/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m321s\u001b[0m 188ms/step - loss: 1.7143 - root_mean_squared_error: 1.2955 - val_loss: 1.9576 - val_root_mean_squared_error: 1.3865\n",
      "Epoch 4/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m308s\u001b[0m 180ms/step - loss: 1.5578 - root_mean_squared_error: 1.2340 - val_loss: 1.9376 - val_root_mean_squared_error: 1.3795\n",
      "Epoch 5/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m224s\u001b[0m 134ms/step - loss: 1.4274 - root_mean_squared_error: 1.1802 - val_loss: 1.9344 - val_root_mean_squared_error: 1.3785\n",
      "Epoch 6/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 119ms/step - loss: 1.3002 - root_mean_squared_error: 1.1252 - val_loss: 1.9345 - val_root_mean_squared_error: 1.3787\n",
      "Epoch 7/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m243s\u001b[0m 145ms/step - loss: 1.2128 - root_mean_squared_error: 1.0859 - val_loss: 1.9420 - val_root_mean_squared_error: 1.3817\n",
      "Epoch 8/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 146ms/step - loss: 1.1415 - root_mean_squared_error: 1.0529 - val_loss: 1.9400 - val_root_mean_squared_error: 1.3812\n",
      "Epoch 9/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m285s\u001b[0m 160ms/step - loss: 1.0845 - root_mean_squared_error: 1.0258 - val_loss: 1.9468 - val_root_mean_squared_error: 1.3839\n",
      "Epoch 10/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m253s\u001b[0m 151ms/step - loss: 1.0464 - root_mean_squared_error: 1.0074 - val_loss: 1.9494 - val_root_mean_squared_error: 1.3851\n",
      "Epoch 11/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 157ms/step - loss: 1.0143 - root_mean_squared_error: 0.9917 - val_loss: 1.9519 - val_root_mean_squared_error: 1.3863\n",
      "Epoch 12/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m272s\u001b[0m 163ms/step - loss: 0.9740 - root_mean_squared_error: 0.9716 - val_loss: 1.9518 - val_root_mean_squared_error: 1.3865\n",
      "Epoch 13/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 157ms/step - loss: 0.9467 - root_mean_squared_error: 0.9578 - val_loss: 1.9566 - val_root_mean_squared_error: 1.3885\n",
      "Epoch 14/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m247s\u001b[0m 147ms/step - loss: 0.9188 - root_mean_squared_error: 0.9436 - val_loss: 1.9643 - val_root_mean_squared_error: 1.3915\n",
      "Epoch 15/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m246s\u001b[0m 138ms/step - loss: 0.8866 - root_mean_squared_error: 0.9267 - val_loss: 1.9547 - val_root_mean_squared_error: 1.3883\n",
      "Epoch 16/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 139ms/step - loss: 0.8685 - root_mean_squared_error: 0.9172 - val_loss: 1.9573 - val_root_mean_squared_error: 1.3895\n",
      "Epoch 17/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 150ms/step - loss: 0.8411 - root_mean_squared_error: 0.9025 - val_loss: 1.9578 - val_root_mean_squared_error: 1.3899\n",
      "Epoch 18/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 149ms/step - loss: 0.8167 - root_mean_squared_error: 0.8893 - val_loss: 1.9564 - val_root_mean_squared_error: 1.3896\n",
      "Epoch 19/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 149ms/step - loss: 0.8051 - root_mean_squared_error: 0.8830 - val_loss: 1.9670 - val_root_mean_squared_error: 1.3936\n",
      "Epoch 20/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 150ms/step - loss: 0.7822 - root_mean_squared_error: 0.8703 - val_loss: 1.9675 - val_root_mean_squared_error: 1.3940\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7f21f98c1b50>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "rating = layers.Concatenate()([user_emb, rest_emb])\n",
    "rating = layers.Add()([rating, user_bias, rest_bias])\n",
    "\n",
    "# redusing the first dense layer into 15 neurons and adding a l2 regularization\n",
    "rating = layers.Dense(15, activation='relu',kernel_regularizer=regularizers.l2(1e-3))(rating)\n",
    "# creating a dropout layer\n",
    "rating = layers.Dropout(0.3)(rating)\n",
    "# output layer\n",
    "rating = layers.Dense(1, activation='sigmoid')(rating)\n",
    "#convertion of output rating\n",
    "rating = layers.Lambda(lambda x:x*(max_rating - min_rating) + min_rating)(rating)\n",
    "\n",
    "model_1 = models.Model([user, restaurant], rating)\n",
    "\n",
    "# Compile the model\n",
    "model_1.compile( optimizer='sgd', loss='mse',  metrics=[metrics.RootMeanSquaredError()])\n",
    "\n",
    "# Train the model\n",
    "model_1.fit(x=[X_train[:,0], X_train[:,1]], y=y_train,\n",
    "            batch_size=256,\n",
    "            epochs=20, \n",
    "            verbose=1,\n",
    "            validation_data=([X_test[:,0], X_test[:,1]], y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The second model has performed worse than the first with a higher rmse score and the model is overfitting the training data i.e it has a good train score but poor validation score.\n",
    "\n",
    "we will try and simplify the model further. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m237s\u001b[0m 141ms/step - loss: 1.6052 - root_mean_squared_error: 1.2586 - val_loss: 1.9195 - val_root_mean_squared_error: 1.3816\n",
      "Epoch 2/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m250s\u001b[0m 133ms/step - loss: 1.1682 - root_mean_squared_error: 1.0758 - val_loss: 1.9942 - val_root_mean_squared_error: 1.4083\n",
      "Epoch 3/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 134ms/step - loss: 1.1277 - root_mean_squared_error: 1.0568 - val_loss: 2.0187 - val_root_mean_squared_error: 1.4170\n",
      "Epoch 4/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 133ms/step - loss: 1.0749 - root_mean_squared_error: 1.0315 - val_loss: 2.0266 - val_root_mean_squared_error: 1.4198\n",
      "Epoch 5/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 133ms/step - loss: 1.0261 - root_mean_squared_error: 1.0076 - val_loss: 2.0196 - val_root_mean_squared_error: 1.4173\n",
      "Epoch 6/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 131ms/step - loss: 0.9996 - root_mean_squared_error: 0.9943 - val_loss: 2.0127 - val_root_mean_squared_error: 1.4149\n",
      "Epoch 7/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 131ms/step - loss: 0.9819 - root_mean_squared_error: 0.9854 - val_loss: 2.0153 - val_root_mean_squared_error: 1.4158\n",
      "Epoch 8/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 132ms/step - loss: 0.9672 - root_mean_squared_error: 0.9779 - val_loss: 2.0235 - val_root_mean_squared_error: 1.4187\n",
      "Epoch 9/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m218s\u001b[0m 130ms/step - loss: 0.9527 - root_mean_squared_error: 0.9705 - val_loss: 2.0196 - val_root_mean_squared_error: 1.4173\n",
      "Epoch 10/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 131ms/step - loss: 0.9368 - root_mean_squared_error: 0.9622 - val_loss: 2.0274 - val_root_mean_squared_error: 1.4200\n",
      "Epoch 11/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m265s\u001b[0m 132ms/step - loss: 0.9351 - root_mean_squared_error: 0.9614 - val_loss: 2.0260 - val_root_mean_squared_error: 1.4196\n",
      "Epoch 12/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 131ms/step - loss: 0.9181 - root_mean_squared_error: 0.9525 - val_loss: 2.0289 - val_root_mean_squared_error: 1.4206\n",
      "Epoch 13/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m221s\u001b[0m 132ms/step - loss: 0.9169 - root_mean_squared_error: 0.9519 - val_loss: 2.0294 - val_root_mean_squared_error: 1.4208\n",
      "Epoch 14/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 132ms/step - loss: 0.9117 - root_mean_squared_error: 0.9491 - val_loss: 2.0364 - val_root_mean_squared_error: 1.4232\n",
      "Epoch 15/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m270s\u001b[0m 137ms/step - loss: 0.9107 - root_mean_squared_error: 0.9486 - val_loss: 2.0373 - val_root_mean_squared_error: 1.4235\n",
      "Epoch 16/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m251s\u001b[0m 130ms/step - loss: 0.8983 - root_mean_squared_error: 0.9420 - val_loss: 2.0377 - val_root_mean_squared_error: 1.4237\n",
      "Epoch 17/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m219s\u001b[0m 131ms/step - loss: 0.8937 - root_mean_squared_error: 0.9396 - val_loss: 2.0325 - val_root_mean_squared_error: 1.4218\n",
      "Epoch 18/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m220s\u001b[0m 131ms/step - loss: 0.8919 - root_mean_squared_error: 0.9386 - val_loss: 2.0342 - val_root_mean_squared_error: 1.4224\n",
      "Epoch 19/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m222s\u001b[0m 133ms/step - loss: 0.8839 - root_mean_squared_error: 0.9343 - val_loss: 2.0392 - val_root_mean_squared_error: 1.4242\n",
      "Epoch 20/20\n",
      "\u001b[1m1673/1673\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m221s\u001b[0m 132ms/step - loss: 0.8778 - root_mean_squared_error: 0.9310 - val_loss: 2.0367 - val_root_mean_squared_error: 1.4233\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7f22018e22d0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "rating = layers.Concatenate()([user_emb, rest_emb])\n",
    "# Adds the user and restaurant embedding to the dot product of the embeddings\n",
    "rating = layers.Add()([rating, user_bias, rest_bias])\n",
    "\n",
    "# reducing the first layer further to 10 node\n",
    "rating = layers.Dense(10, activation='relu')(rating)\n",
    "# increasing the dropout rate to 0.2\n",
    "rating = layers.Dropout(0.6)(rating)\n",
    "# output layer\n",
    "rating = layers.Dense(1, activation='sigmoid')(rating)\n",
    "# conertion of output rating\n",
    "rating = layers.Lambda(lambda x:x*(max_rating - min_rating) + min_rating)(rating)\n",
    "\n",
    "model_2 = models.Model([user, restaurant], rating)\n",
    "\n",
    "# Compile the model\n",
    "model_2.compile( optimizer= 'sgd',\n",
    "                loss='mse', \n",
    "                metrics= [metrics.RootMeanSquaredError()])\n",
    "\n",
    "# Train the model\n",
    "model_2.fit(x=[X_train[:,0], X_train[:,1]], y=y_train,\n",
    "            batch_size=256, \n",
    "            epochs=20, \n",
    "            verbose=1,\n",
    "            validation_data=([X_test[:,0], X_test[:,1]], y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The third model has further overfitted the training data as it has high validation score and low training score.\n",
    "Therefore our best neural model is baseline model which has a validation score of 1.394."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: \n",
      "\u001b[1m13380/13380\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m359s\u001b[0m 27ms/step - loss: 1.1958 - root_mean_squared_error: 1.0885\n",
      "[1.1989389657974243, 1.089964509010315]\n",
      "Testing data: \n",
      "\u001b[1m3345/3345\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 27ms/step - loss: 1.7751 - root_mean_squared_error: 1.3282\n",
      "[1.779281497001648, 1.3298012018203735]\n"
     ]
    }
   ],
   "source": [
    "# evaluating the best model on the training data\n",
    "print(\"Training data: \")\n",
    "print(baseline_model.evaluate([X_train[:,0], X_train[:,1]], y_train))\n",
    "\n",
    "# evaluating the best model on the test data\n",
    "print(\"Testing data: \")\n",
    "print(baseline_model.evaluate([X_test[:,0], X_test[:,1]], y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "***\n",
    "\n",
    "> The baseline model has a training RMSE of 1.0885 and a test RMSE of 1.3282 hence being our better neural networks model with the lowest test scores.\n",
    "\n",
    "> In all the models SVD has emerged to be the best RMSE score of 0.068"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## 5. EVALUATION\n",
    "***\n",
    "\n",
    "### FINDINGS AND EVALUATION BASED ON EDA\n",
    "***\n",
    "\n",
    "1) Most common ratings are 3.5 and 4.0 stars, indicating general satisfaction.Ratings below 2.5 stars are uncommon, showing few very poor experiences.\n",
    "\n",
    "2) Pennsylvania (PA), Florida (FL) and Tennessee (TN) boast the highest numbers of restaurants among the states, highlighting them as major markets for the restaurant industry.\n",
    "\n",
    "3) 66.9% Open Restaurants: A significant majority of the restaurants in our dataset are currently operational. This proportion suggests a robust industry where the majority of listed entities are active, presenting numerous options for recommendations.\n",
    "\n",
    "4) 33.1% Closed Restaurants: A substantial portion of the restaurants are no longer in operation. This data is crucial for ensuring accuracy in our recommendation system by filtering out closed establishments, thereby improving user experience and system reliability.\n",
    "\n",
    "5) McDonald's, Subway, and Taco Bell lead in frequency, indicating a high presence of these chains within the dataset. This suggests a dominance of fast-food chains in the restaurant landscape.\n",
    "\n",
    "6) The highest review counts appear at 4.0 and 5.0 star ratings, suggesting high customer engagement with top-rated restaurants and the opposite is true.\n",
    "\n",
    "7) Seasonal Trends: Frequent peaks between April and July, indicating increased customer engagement during this period.\n",
    "\n",
    "8) Decline in Engagement: Noticeable drops in reviews occur after July, particularly in September and December.\n",
    "\n",
    "9) **Covid Impact**: Evidence of declined restaurnat reviews in the year 2020 due to the COVID outbreak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL FINDINGS AND EVALUATION\n",
    "***\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) COLLABORATIVE  BASED MODELS\n",
    "\n",
    "##### i) BASELINE MODEL  - NORMAL PREDICTOR\n",
    "\n",
    "This model was initialized and evaluated using RMSE, yielding a poor performance with an RMSE of 0.8192.\n",
    "\n",
    "##### ii) NMF(Non-Negative Matrix Factorization) MODEL\n",
    "\n",
    "The NMF model had an RMSE of 0.3489 which shows improved model perfomance.\n",
    "\n",
    "##### iii) SVD (Single Value Decomposition) MODEL\n",
    "\n",
    "The vanilla SVD model had an RMSE of 0.1171, indicating good accuracy. Cross-validation further improved this to 0.1135. To enhance performance, hyperparameter tuning was performed using GridSearchCV to find the optimal parameters, improving the model further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) DEEP NEURAL NETWORKS (DNN)\n",
    "\n",
    "The text outlines the development and evaluation of deep neural network (DNN) models for a recommendation system using Keras. Three models were tested:\n",
    "\n",
    "1. **Vanilla Model**: This model has a three-layer architecture and was trained for 10 epochs. RMSE improved gradually from 1.5015 to 1.4409, with a validation RMSE of 1.4512 by the end.\n",
    "\n",
    "2. **Tuned Model 1**: This model added user and restaurant embeddings, L2 regularization, and a dropout layer to reduce overfitting. After 20 epochs, it achieved a training RMSE of 0.8703 but showed signs of overfitting with a validation RMSE of 1.3940.\n",
    "\n",
    "3. **Tuned Model 2**: Similar to Model 1, but with a higher dropout rate. The training RMSE improved to 0.9310, but the validation RMSE remained high at 1.4233, indicating overfitting.\n",
    "\n",
    "**Conclusion**: The third model overfitted the training data, leading to poor generalization. The best performing model was the vanilla model with a validation RMSE of 1.3179, but it still performed worse than the optimized SVD model.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RECOMMENDATIONS\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) **Cuisine diversification** \n",
    "\n",
    "2) **Prioritize major markets**\n",
    "\n",
    "3) **Tailor recommendations to Market Size** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FUTURE WORKS\n",
    "***\n",
    "\n",
    "1) **Expansion of Dataset** Eg Include More Cuisines,Crowdsourced Data\n",
    "\n",
    "2) **Enhanced User Interface and Experience** Eg Augmented Reality (AR), Voice Interaction\n",
    "3) **Integration with Other Services** Eg Reservation Systems\n",
    "\n",
    "4) **Feedback and Continuous Improvement** Eg User Feedback Loops, A/B Testing\n",
    "\n",
    "5) **Expanding Geographical Coverage for Data Collection**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## REFERENCES\n",
    "***\n",
    "\n",
    "1) [YELP](https://www.yelp.com/dataset/download)\n",
    "\n",
    "2) [Streamlit](https://docs.streamlit.io/)"
   ]
  }
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