Correlation-based Recommendation System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation-based Recommendation System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Correlation-based recommendation uses Pearson's r-correlation to recommend an item, that is most simmilar to the item, a user has already chosen.\n",
    "\n",
    "\n",
    "* Pearson's r-correlation coefficient(r) is a measure of linear correlation between two variables\n",
    "\n",
    "> r = 1  : Strong positive relationship\n",
    "\n",
    "> r = 0  : Not linearly correlated\n",
    "\n",
    "> r = -1 : Strong negative linear correlation\n",
    "\n",
    "* Correlation-based Recommendation System recommends item based on how well the item is correlated to other items, with respect to user-ratings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame_rating = pd.read_csv(\"rating_final.csv\")\n",
    "frame_cuisine = pd.read_csv(\"chefmozcuisine.csv\")\n",
    "frame_geo = pd.read_excel(\"geoplaces2.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's have a closer look at the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userID</th>\n",
       "      <th>placeID</th>\n",
       "      <th>rating</th>\n",
       "      <th>food_rating</th>\n",
       "      <th>service_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U1077</td>\n",
       "      <td>135085</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U1077</td>\n",
       "      <td>135038</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U1077</td>\n",
       "      <td>132825</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U1077</td>\n",
       "      <td>135060</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U1068</td>\n",
       "      <td>135104</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  userID  placeID  rating  food_rating  service_rating\n",
       "0  U1077   135085       2            2               2\n",
       "1  U1077   135038       2            2               1\n",
       "2  U1077   132825       2            2               2\n",
       "3  U1077   135060       1            2               2\n",
       "4  U1068   135104       1            1               2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135110</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>135109</td>\n",
       "      <td>Italian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135107</td>\n",
       "      <td>Latin_American</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135106</td>\n",
       "      <td>Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135105</td>\n",
       "      <td>Fast_Food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID        Rcuisine\n",
       "0   135110         Spanish\n",
       "1   135109         Italian\n",
       "2   135107  Latin_American\n",
       "3   135106         Mexican\n",
       "4   135105       Fast_Food"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_cuisine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>the_geom_meter</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>fax</th>\n",
       "      <th>...</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>smoking_area</th>\n",
       "      <th>dress_code</th>\n",
       "      <th>accessibility</th>\n",
       "      <th>price</th>\n",
       "      <th>url</th>\n",
       "      <th>Rambience</th>\n",
       "      <th>franchise</th>\n",
       "      <th>area</th>\n",
       "      <th>other_services</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134999</td>\n",
       "      <td>18.915421</td>\n",
       "      <td>-99.184871</td>\n",
       "      <td>0101000020957F000088568DE356715AC138C0A525FC46...</td>\n",
       "      <td>Kiku Cuernavaca</td>\n",
       "      <td>Revolucion</td>\n",
       "      <td>Cuernavaca</td>\n",
       "      <td>Morelos</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>none</td>\n",
       "      <td>informal</td>\n",
       "      <td>no_accessibility</td>\n",
       "      <td>medium</td>\n",
       "      <td>kikucuernavaca.com.mx</td>\n",
       "      <td>familiar</td>\n",
       "      <td>f</td>\n",
       "      <td>closed</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>132825</td>\n",
       "      <td>22.147392</td>\n",
       "      <td>-100.983092</td>\n",
       "      <td>0101000020957F00001AD016568C4858C1243261274BA5...</td>\n",
       "      <td>puesto de tacos</td>\n",
       "      <td>esquina santos degollado y leon guzman</td>\n",
       "      <td>s.l.p.</td>\n",
       "      <td>s.l.p.</td>\n",
       "      <td>mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>none</td>\n",
       "      <td>informal</td>\n",
       "      <td>completely</td>\n",
       "      <td>low</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>f</td>\n",
       "      <td>open</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135106</td>\n",
       "      <td>22.149709</td>\n",
       "      <td>-100.976093</td>\n",
       "      <td>0101000020957F0000649D6F21634858C119AE9BF528A3...</td>\n",
       "      <td>El RincÛn de San Francisco</td>\n",
       "      <td>Universidad 169</td>\n",
       "      <td>San Luis Potosi</td>\n",
       "      <td>San Luis Potosi</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>Wine-Beer</td>\n",
       "      <td>only at bar</td>\n",
       "      <td>informal</td>\n",
       "      <td>partially</td>\n",
       "      <td>medium</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>f</td>\n",
       "      <td>open</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132667</td>\n",
       "      <td>23.752697</td>\n",
       "      <td>-99.163359</td>\n",
       "      <td>0101000020957F00005D67BCDDED8157C1222A2DC8D84D...</td>\n",
       "      <td>little pizza Emilio Portes Gil</td>\n",
       "      <td>calle emilio portes gil</td>\n",
       "      <td>victoria</td>\n",
       "      <td>tamaulipas</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>none</td>\n",
       "      <td>informal</td>\n",
       "      <td>completely</td>\n",
       "      <td>low</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>t</td>\n",
       "      <td>closed</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>132613</td>\n",
       "      <td>23.752903</td>\n",
       "      <td>-99.165076</td>\n",
       "      <td>0101000020957F00008EBA2D06DC8157C194E03B7B504E...</td>\n",
       "      <td>carnitas_mata</td>\n",
       "      <td>lic. Emilio portes gil</td>\n",
       "      <td>victoria</td>\n",
       "      <td>Tamaulipas</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>?</td>\n",
       "      <td>...</td>\n",
       "      <td>No_Alcohol_Served</td>\n",
       "      <td>permitted</td>\n",
       "      <td>informal</td>\n",
       "      <td>completely</td>\n",
       "      <td>medium</td>\n",
       "      <td>?</td>\n",
       "      <td>familiar</td>\n",
       "      <td>t</td>\n",
       "      <td>closed</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID   latitude   longitude  \\\n",
       "0   134999  18.915421  -99.184871   \n",
       "1   132825  22.147392 -100.983092   \n",
       "2   135106  22.149709 -100.976093   \n",
       "3   132667  23.752697  -99.163359   \n",
       "4   132613  23.752903  -99.165076   \n",
       "\n",
       "                                      the_geom_meter  \\\n",
       "0  0101000020957F000088568DE356715AC138C0A525FC46...   \n",
       "1  0101000020957F00001AD016568C4858C1243261274BA5...   \n",
       "2  0101000020957F0000649D6F21634858C119AE9BF528A3...   \n",
       "3  0101000020957F00005D67BCDDED8157C1222A2DC8D84D...   \n",
       "4  0101000020957F00008EBA2D06DC8157C194E03B7B504E...   \n",
       "\n",
       "                             name                                 address  \\\n",
       "0                 Kiku Cuernavaca                              Revolucion   \n",
       "1                 puesto de tacos  esquina santos degollado y leon guzman   \n",
       "2      El RincÛn de San Francisco                         Universidad 169   \n",
       "3  little pizza Emilio Portes Gil                 calle emilio portes gil   \n",
       "4                   carnitas_mata                  lic. Emilio portes gil   \n",
       "\n",
       "              city            state country fax  ...            alcohol  \\\n",
       "0       Cuernavaca          Morelos  Mexico   ?  ...  No_Alcohol_Served   \n",
       "1           s.l.p.           s.l.p.  mexico   ?  ...  No_Alcohol_Served   \n",
       "2  San Luis Potosi  San Luis Potosi  Mexico   ?  ...          Wine-Beer   \n",
       "3        victoria        tamaulipas       ?   ?  ...  No_Alcohol_Served   \n",
       "4         victoria       Tamaulipas  Mexico   ?  ...  No_Alcohol_Served   \n",
       "\n",
       "  smoking_area dress_code     accessibility   price                    url  \\\n",
       "0         none   informal  no_accessibility  medium  kikucuernavaca.com.mx   \n",
       "1         none   informal        completely     low                      ?   \n",
       "2  only at bar   informal         partially  medium                      ?   \n",
       "3         none   informal        completely     low                      ?   \n",
       "4    permitted   informal        completely  medium                      ?   \n",
       "\n",
       "  Rambience franchise    area other_services  \n",
       "0  familiar         f  closed           none  \n",
       "1  familiar         f    open           none  \n",
       "2  familiar         f    open           none  \n",
       "3  familiar         t  closed           none  \n",
       "4  familiar         t  closed           none  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_geo.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We only need the placeID and the name of the places that is being reviewed.\n",
    "\n",
    "\n",
    "Let's take the subset of the frame_geo, by taking only \"placeID\" and \"name\" variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>134999</td>\n",
       "      <td>Kiku Cuernavaca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>132825</td>\n",
       "      <td>puesto de tacos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135106</td>\n",
       "      <td>El RincÛn de San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132667</td>\n",
       "      <td>little pizza Emilio Portes Gil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>132613</td>\n",
       "      <td>carnitas_mata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   placeID                            name\n",
       "0   134999                 Kiku Cuernavaca\n",
       "1   132825                 puesto de tacos\n",
       "2   135106      El RincÛn de San Francisco\n",
       "3   132667  little pizza Emilio Portes Gil\n",
       "4   132613                   carnitas_mata"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "places_ = frame_geo[[\"placeID\", \"name\"]]\n",
    "places_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping and Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a new dataframe to see the average rating that was given to each place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>placeID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132560</th>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132561</th>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132564</th>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132572</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132583</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         rating\n",
       "placeID        \n",
       "132560     0.50\n",
       "132561     0.75\n",
       "132564     1.25\n",
       "132572     1.00\n",
       "132583     1.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rating = pd.DataFrame(frame_rating.groupby(\"placeID\")[\"rating\"].mean())\n",
    "df_rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to check the popularity of each place. We will check the popularity based on the number of ratings each place has received, and we will add that column to the df_rating dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>rating</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>placeID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132560</th>\n",
       "      <td>0.50</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132561</th>\n",
       "      <td>0.75</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132564</th>\n",
       "      <td>1.25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132572</th>\n",
       "      <td>1.00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132583</th>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating  rating_count\n",
       "placeID                      \n",
       "132560     0.50             4\n",
       "132561     0.75             4\n",
       "132564     1.25             4\n",
       "132572     1.00            15\n",
       "132583     1.00             4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rating[\"rating_count\"] = pd.DataFrame(\n",
    "    frame_rating.groupby(\"placeID\")[\"rating\"].count())\n",
    "\n",
    "df_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>rating</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>130.000000</td>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.179622</td>\n",
       "      <td>8.930769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.349354</td>\n",
       "      <td>6.124279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.181818</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rating  rating_count\n",
       "count  130.000000    130.000000\n",
       "mean     1.179622      8.930769\n",
       "std      0.349354      6.124279\n",
       "min      0.250000      3.000000\n",
       "25%      1.000000      5.000000\n",
       "50%      1.181818      7.000000\n",
       "75%      1.400000     11.000000\n",
       "max      2.000000     36.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rating.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>rating</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>placeID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>135085</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132825</th>\n",
       "      <td>1.281250</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135032</th>\n",
       "      <td>1.178571</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135052</th>\n",
       "      <td>1.280000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132834</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rating  rating_count\n",
       "placeID                        \n",
       "135085   1.333333            36\n",
       "132825   1.281250            32\n",
       "135032   1.178571            28\n",
       "135052   1.280000            25\n",
       "132834   1.000000            25"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rating.sort_values(\"rating_count\", ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which place got the highest ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>placeID</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>135085</td>\n",
       "      <td>Tortas Locas Hipocampo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     placeID                    name\n",
       "121   135085  Tortas Locas Hipocampo"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "places_[places_[\"placeID\"] == 135085]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which cuisine got the highest rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>placeID</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>135085</td>\n",
       "      <td>Fast_Food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    placeID   Rcuisine\n",
       "44   135085  Fast_Food"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_cuisine[frame_cuisine[\"placeID\"] == 135085]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We understood that the most popular restaurant is \"Tortas Locas Hipocampo\" and it serves \"Fast Food\" cuisine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing data for the Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "places_crosstab = pd.pivot_table(data = frame_rating, values = \"rating\", \n",
    "                                 index = \"userID\", columns = \"placeID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>placeID</th>\n",
       "      <th>132560</th>\n",
       "      <th>132561</th>\n",
       "      <th>132564</th>\n",
       "      <th>132572</th>\n",
       "      <th>132583</th>\n",
       "      <th>132584</th>\n",
       "      <th>132594</th>\n",
       "      <th>132608</th>\n",
       "      <th>132609</th>\n",
       "      <th>132613</th>\n",
       "      <th>...</th>\n",
       "      <th>135080</th>\n",
       "      <th>135081</th>\n",
       "      <th>135082</th>\n",
       "      <th>135085</th>\n",
       "      <th>135086</th>\n",
       "      <th>135088</th>\n",
       "      <th>135104</th>\n",
       "      <th>135106</th>\n",
       "      <th>135108</th>\n",
       "      <th>135109</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>U1001</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1002</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1004</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1005</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "placeID  132560  132561  132564  132572  132583  132584  132594  132608  \\\n",
       "userID                                                                    \n",
       "U1001       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "U1002       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "U1003       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "U1004       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "U1005       NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "placeID  132609  132613  ...  135080  135081  135082  135085  135086  135088  \\\n",
       "userID                   ...                                                   \n",
       "U1001       NaN     NaN  ...     NaN     NaN     NaN     0.0     NaN     NaN   \n",
       "U1002       NaN     NaN  ...     NaN     NaN     NaN     1.0     NaN     NaN   \n",
       "U1003       NaN     NaN  ...     2.0     NaN     NaN     NaN     NaN     NaN   \n",
       "U1004       NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "U1005       NaN     NaN  ...     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "\n",
       "placeID  135104  135106  135108  135109  \n",
       "userID                                   \n",
       "U1001       NaN     NaN     NaN     NaN  \n",
       "U1002       NaN     1.0     NaN     NaN  \n",
       "U1003       NaN     NaN     NaN     NaN  \n",
       "U1004       NaN     2.0     NaN     NaN  \n",
       "U1005       NaN     NaN     NaN     NaN  \n",
       "\n",
       "[5 rows x 130 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "places_crosstab.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is visible that, above matrix has a lot of NaN values. This is because, not many people review places.\n",
    "\n",
    "We need to isolate the user ratings from the restaurant called \"Tortas Locas Hipocampo\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Tortas Locas Hipocampo\" is the most popular place. So let's create a filter where the rating for this restaurants are greater than or equal to zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userID\n",
       "U1001    0.0\n",
       "U1002    1.0\n",
       "U1007    1.0\n",
       "U1013    1.0\n",
       "U1016    2.0\n",
       "U1027    1.0\n",
       "U1029    1.0\n",
       "U1032    1.0\n",
       "U1033    2.0\n",
       "U1036    2.0\n",
       "U1045    2.0\n",
       "U1046    1.0\n",
       "U1049    0.0\n",
       "U1056    2.0\n",
       "U1059    2.0\n",
       "U1062    0.0\n",
       "U1077    2.0\n",
       "U1081    1.0\n",
       "U1084    2.0\n",
       "U1086    2.0\n",
       "U1089    1.0\n",
       "U1090    2.0\n",
       "U1092    0.0\n",
       "U1098    1.0\n",
       "U1104    2.0\n",
       "U1106    2.0\n",
       "U1108    1.0\n",
       "U1109    2.0\n",
       "U1113    1.0\n",
       "U1116    2.0\n",
       "U1120    0.0\n",
       "U1122    2.0\n",
       "U1132    2.0\n",
       "U1134    2.0\n",
       "U1135    0.0\n",
       "U1137    2.0\n",
       "Name: 135085, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tortas_rating = places_crosstab[135085]\n",
    "Tortas_rating[Tortas_rating >= 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above output gives us all the 36 ratings that the restaurant got, that ranges from 0 to 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating correlation based on simmilarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>PearsonR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>placeID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132572</th>\n",
       "      <td>-0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132723</th>\n",
       "      <td>0.301511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132754</th>\n",
       "      <td>0.930261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132825</th>\n",
       "      <td>0.700745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132834</th>\n",
       "      <td>0.814823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PearsonR\n",
       "placeID          \n",
       "132572  -0.428571\n",
       "132723   0.301511\n",
       "132754   0.930261\n",
       "132825   0.700745\n",
       "132834   0.814823"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_To_Tortas = places_crosstab.corrwith(Tortas_rating)\n",
    "corr_Tortas = pd.DataFrame(similar_To_Tortas, columns = [\"PearsonR\"])\n",
    "corr_Tortas.dropna(inplace = True)\n",
    "corr_Tortas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In addition to how well their review score correlate with \"Tortas Locas Hipocampo\", we also need to take in consideration how popular is the place.\n",
    "\n",
    "In other words, we cannot say any place to have similar score like \"Tortas Locas Hipocampo\", if that place only has, let's say 2 ratings. We have to consider the number of reviews that the places have got before interpreting the correlation between the review_score of \"Tortas Locas Hipocampo\" and other places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tortas_corr_summary = corr_Tortas.join(df_rating[\"rating_count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PearsonR</th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>placeID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>135076</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135085</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135066</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132754</th>\n",
       "      <td>0.930261</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135045</th>\n",
       "      <td>0.912871</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135062</th>\n",
       "      <td>0.898933</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135028</th>\n",
       "      <td>0.892218</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135042</th>\n",
       "      <td>0.881409</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135046</th>\n",
       "      <td>0.867722</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132872</th>\n",
       "      <td>0.840168</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PearsonR  rating_count\n",
       "placeID                        \n",
       "135076   1.000000            13\n",
       "135085   1.000000            36\n",
       "135066   1.000000            12\n",
       "132754   0.930261            13\n",
       "135045   0.912871            13\n",
       "135062   0.898933            21\n",
       "135028   0.892218            15\n",
       "135042   0.881409            20\n",
       "135046   0.867722            11\n",
       "132872   0.840168            12"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tortas_corr_summary[Tortas_corr_summary[\"rating_count\"] >= 10].sort_values(\"PearsonR\", ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pearsonR values of 1  aren't meaningful. This is because, for those places, there is only one user who reviewed both the places\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "places_corr_Tortas = pd.DataFrame([135085, 132754, 135045, 135062, 135028,135042, 135046],\n",
    "                                 index = np.arange(7),\n",
    "                                 columns = [\"placeID\"])\n",
    "\n",
    "summary = pd.merge(places_corr_Tortas, frame_cuisine, on = \"placeID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>placeID</th>\n",
       "      <th>Rcuisine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135085</td>\n",
       "      <td>Fast_Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>132754</td>\n",
       "      <td>Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>135028</td>\n",
       "      <td>Mexican</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>135042</td>\n",
       "      <td>Chinese</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>135046</td>\n",
       "      <td>Fast_Food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   placeID   Rcuisine\n",
       "0   135085  Fast_Food\n",
       "1   132754    Mexican\n",
       "2   135028    Mexican\n",
       "3   135042    Chinese\n",
       "4   135046  Fast_Food"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>placeID</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>135046</td>\n",
       "      <td>Restaurante El Reyecito</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    placeID                     name\n",
       "42   135046  Restaurante El Reyecito"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "places_[places_[\"placeID\"]==135046]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         916\n",
       "unique         59\n",
       "top       Mexican\n",
       "freq          239\n",
       "Name: Rcuisine, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_cuisine[\"Rcuisine\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    ">According to the decribe method of the Rcuisine variable in our frame_cuisine dataset, we have 59 unique cuisines, with Mexican having the highest frequency.\n",
    "\n",
    ">Considering that there are 59 total cuisine types that could have been offered, and that we got back another fast food place in our top six most similar places, it looks like our correlation based recommendation system is on track."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  }
 ],
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