Collaborative Filtering.ipynb

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    "- Author: Kemil Herath \n",
    "- MIS 753: Independent Study, Fall 2021\n",
    "\n",
    "## Collaborative filtering \n",
    "- For this project I'm using the yelp dataset: \n",
    "https://www.yelp.com/dataset\n",
    "\n",
    "- A subset of the businessness data and reviews data is used. \n",
    "\n",
    "#### Sources: \n",
    "- https://predictivehacks.com/item-based-collaborative-filtering-in-python/\n",
    "- https://chrisalbon.com/code/machine_learning/feature_engineering/select_best_number_of_components_in_tsvd/\n",
    "- https://realpython.com/build-recommendation-engine-collaborative-filtering/"
   ]
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   "execution_count": 1,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "## Load Libraries \n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import string \n",
    "from wordcloud import WordCloud\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "import random\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "import re \n",
    "\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": 2,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_name</th>\n",
       "      <th>business_id</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>service_categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boruboru - Sandy Springs</td>\n",
       "      <td>kPiQ9kI_eP6LUob-Pv6AWg</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Japanese, Sushi Bars, Food, Poke, Ramen, Resta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smokey Bones Bar &amp; Fire Grill</td>\n",
       "      <td>K-TMzK7eKQT4cAUoy_IlSw</td>\n",
       "      <td>Stoughton</td>\n",
       "      <td>MA</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Barbeque, Burgers, Restaurants, American (Trad...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dugans</td>\n",
       "      <td>mjJI-DFchylyZHB0b3n2lA</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Bars, American (Traditional), Restaurants, Nig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China Station</td>\n",
       "      <td>3YsJCNhI4Vw7OtweFN622w</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Chinese, Restaurants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ben Hill Grill</td>\n",
       "      <td>cOo7OHZinS8hn5P_-tFVKA</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Seafood, Fast Food, Vegan, American (Tradition...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                   business_name             business_id       city state  \\\n",
       "0       Boruboru - Sandy Springs  kPiQ9kI_eP6LUob-Pv6AWg    Atlanta    GA   \n",
       "1  Smokey Bones Bar & Fire Grill  K-TMzK7eKQT4cAUoy_IlSw  Stoughton    MA   \n",
       "2                         Dugans  mjJI-DFchylyZHB0b3n2lA    Atlanta    GA   \n",
       "3                  China Station  3YsJCNhI4Vw7OtweFN622w     Boston    MA   \n",
       "4                 Ben Hill Grill  cOo7OHZinS8hn5P_-tFVKA    Atlanta    GA   \n",
       "\n",
       "   star_rating                                 service_categories  \n",
       "0          4.5  Japanese, Sushi Bars, Food, Poke, Ramen, Resta...  \n",
       "1          2.5  Barbeque, Burgers, Restaurants, American (Trad...  \n",
       "2          2.5  Bars, American (Traditional), Restaurants, Nig...  \n",
       "3          4.0                               Chinese, Restaurants  \n",
       "4          4.5  Seafood, Fast Food, Vegan, American (Tradition...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load businesses data \n",
    "col_names = ['business_name', 'business_id', 'city', 'state', 'star_rating', 'service_categories']\n",
    "business = pd.read_csv('Data/business.csv')\n",
    "business.columns = col_names\n",
    "business.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34196, 6)"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "business.shape"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>business_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
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       "      <td>4.0</td>\n",
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       "                review_id             business_id                 user_id  \\\n",
       "0  J4a2TuhDasjn2k3wWtHZnQ  xGXzsc-hzam-VArK6eTvtw  RNm_RWkcd02Li2mKPRe7Eg   \n",
       "1  9vqwvFCBG3FBiHGmOHMmiA  DbXHNl890xSXNiyRczLWAg  XGkAG92TQ3MQUKGX9sLUhw   \n",
       "2  FdoBFTjXXMn4hVnJ59EtiQ  WQFn1A7-UAA4JT5YWiop_w  eLAYHxHUutiXswy-CfeiUw   \n",
       "3  ucFOnqgaV40oQ2YNyz5ddQ  KXCXaF5qimmtKKqnPc_LQA  JHXQEayrDHOWGexs0dCviA   \n",
       "4  GDgXjXSZCA1iNQWD7OHXfg  mOnesB4IF9j6-ZmHoOHOig  1RCRKuHgP3FskGUVnmFdxg   \n",
       "\n",
       "   rating  \n",
       "0     1.0  \n",
       "1     5.0  \n",
       "2     1.0  \n",
       "3     1.0  \n",
       "4     4.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load reviews Data \n",
    "col_names = ['review_id', 'business_id', 'user_id', 'rating']\n",
    "reviews = pd.read_csv('reviews.csv')\n",
    "reviews.columns = col_names\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250000, 4)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "reviews.shape"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Take samples of the data for faster processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Take a random sample of reviews data for easier processing \n",
    "reviews1 = reviews.sample(frac=0.16, random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>user_id</th>\n",
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       "      <td>4GUk6uuf-9QQIUxJYYPNBA</td>\n",
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       "      <td>5.0</td>\n",
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      "text/plain": [
       "                     review_id             business_id  \\\n",
       "109672  7O6ujyFXDW4DWf4LFOO1fQ  j-JGN1f_cN_20oePpRWXrw   \n",
       "143738  Cg75QYA8b4jypg6GDDn7vQ  OX01EqImVbTGWepgNPNsJA   \n",
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       "248710  z7py0F2cQBJ0nwLzIaCjqA  EGsftsHMWmKF3mc2UQoQug   \n",
       "41081   ocfbk7Rl5TB2otquxlurIw  vQNe7TD_QDpGtGbkaAqi8Q   \n",
       "\n",
       "                       user_id  rating  \n",
       "109672  8KpEEKNjzgHu_zWXA4-cbQ     5.0  \n",
       "143738  BQrLWECGBozqKaUVnv8TYw     5.0  \n",
       "180135  s7qFhPQgZq_po9ffh_Icpg     2.0  \n",
       "248710  4GUk6uuf-9QQIUxJYYPNBA     2.0  \n",
       "41081   LbfAaO0-15DunU_3qKGF4Q     5.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "reviews1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Take a random sampel of businessess data for easier processing \n",
    "business1 = business.sample(frac=0.15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5129, 6)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Processing/ Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
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       "      <th>0</th>\n",
       "      <td>Khob Khun Thai Cuisine</td>\n",
       "      <td>wTVzfhLOYUyd6wY9LJWC2w</td>\n",
       "      <td>dQyXVDO7JYKtgZfsV0xOhA</td>\n",
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       "      <td>Khob Khun Thai Cuisine</td>\n",
       "      <td>wTVzfhLOYUyd6wY9LJWC2w</td>\n",
       "      <td>4jRbtVIieuBbSBJWDhP3jw</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Khob Khun Thai Cuisine</td>\n",
       "      <td>wTVzfhLOYUyd6wY9LJWC2w</td>\n",
       "      <td>WzCU-7Vjvv2eTYx8f85U_A</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>Khob Khun Thai Cuisine</td>\n",
       "      <td>wTVzfhLOYUyd6wY9LJWC2w</td>\n",
       "      <td>Ff_K-dXlwSJwN-IVMpKuOw</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Khob Khun Thai Cuisine</td>\n",
       "      <td>wTVzfhLOYUyd6wY9LJWC2w</td>\n",
       "      <td>GyZRi_VETkrHGQoRKa9gsQ</td>\n",
       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "            business_name             business_id                 user_id  \\\n",
       "0  Khob Khun Thai Cuisine  wTVzfhLOYUyd6wY9LJWC2w  dQyXVDO7JYKtgZfsV0xOhA   \n",
       "1  Khob Khun Thai Cuisine  wTVzfhLOYUyd6wY9LJWC2w  4jRbtVIieuBbSBJWDhP3jw   \n",
       "2  Khob Khun Thai Cuisine  wTVzfhLOYUyd6wY9LJWC2w  WzCU-7Vjvv2eTYx8f85U_A   \n",
       "3  Khob Khun Thai Cuisine  wTVzfhLOYUyd6wY9LJWC2w  Ff_K-dXlwSJwN-IVMpKuOw   \n",
       "4  Khob Khun Thai Cuisine  wTVzfhLOYUyd6wY9LJWC2w  GyZRi_VETkrHGQoRKa9gsQ   \n",
       "\n",
       "   rating  \n",
       "0     5.0  \n",
       "1     5.0  \n",
       "2     4.0  \n",
       "3     5.0  \n",
       "4     1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Merge reviews data and businesses data\n",
    "reviewsData = pd.merge(business1[['business_name', 'business_id']],\n",
    "                       reviews1[['business_id','user_id','rating']],\n",
    "                       on='business_id')\n",
    "\n",
    "reviewsData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3032, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviewsData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_name\n",
       "Lechon                                                 128\n",
       "Barcelona Wine Bar South End                            81\n",
       "Tin Shed Garden Cafe                                    64\n",
       "FLIP burger boutique                                    63\n",
       "Santarpio's Pizza                                       62\n",
       "JINYA Ramen Bar - Austin                                52\n",
       "J & M Diner                                             49\n",
       "H&F Burger                                              48\n",
       "Cooper's Hawk Winery & Restaurant - Waterford Lakes     47\n",
       "Sushi Katana                                            47\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check which restaurent has most number of reviews \n",
    "reviewsData.groupby('business_name')['rating'].count().sort_values(ascending =False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that **Lechon** has the most reviews. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility matrix \n",
    "- U X R matrix where U: Users and R: Restaurants\n",
    "- The values will contain reviews given by each user for each restaurent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews_crosstab = reviewsData.pivot_table('rating', index='user_id', columns='business_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>business_name</th>\n",
       "      <th>&amp;pizza - Harvard Square</th>\n",
       "      <th>19th Hole</th>\n",
       "      <th>5 Star Pizza</th>\n",
       "      <th>African Paradise Restaurant</th>\n",
       "      <th>Alamo Drafthouse Cinema South Lamar</th>\n",
       "      <th>American Fresh Beer Garden</th>\n",
       "      <th>Anaheim Produce</th>\n",
       "      <th>Anna's Hand Cut Donuts</th>\n",
       "      <th>Arby's</th>\n",
       "      <th>Arepas Grill</th>\n",
       "      <th>...</th>\n",
       "      <th>Wok N' Kebab Asian Street Fare</th>\n",
       "      <th>X-Site Grill &amp; Bistro</th>\n",
       "      <th>Yan's China Bistro</th>\n",
       "      <th>Yoko Sushi</th>\n",
       "      <th>Yokohama Teppanyaki</th>\n",
       "      <th>ZAZ</th>\n",
       "      <th>Zen Japanese Grill &amp; Sushi Bar</th>\n",
       "      <th>Zeus Greek Street Food</th>\n",
       "      <th>Zoup!</th>\n",
       "      <th>sandoitchi Pop Up</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-BCHgVhr-mx8iEBbphbTXw</th>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>-CUA8xL-9bCZN3nOi1e_Hw</th>\n",
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       "      <td>NaN</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>-CvJZ3v4XxnzQAIZu5JvxQ</th>\n",
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       "      <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>10 rows × 365 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "business_name           &pizza - Harvard Square  19th Hole  5 Star Pizza  \\\n",
       "user_id                                                                    \n",
       "-1CV3L7RAk34790wXVQu4g                      NaN        NaN           NaN   \n",
       "-3s52C4zL_DHRK0ULG6qtg                      NaN        NaN           NaN   \n",
       "-4RRsux7RIX19uBsVTSsMw                      NaN        NaN           NaN   \n",
       "-5E2DVKUmSqpKPOWAL7sTg                      NaN        NaN           NaN   \n",
       "-6t5bJFJne44N_E7lgdR6g                      NaN        NaN           NaN   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                      NaN        NaN           NaN   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                      NaN        NaN           NaN   \n",
       "-BCHgVhr-mx8iEBbphbTXw                      NaN        NaN           NaN   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                      NaN        NaN           NaN   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                      NaN        NaN           NaN   \n",
       "\n",
       "business_name           African Paradise Restaurant  \\\n",
       "user_id                                               \n",
       "-1CV3L7RAk34790wXVQu4g                          NaN   \n",
       "-3s52C4zL_DHRK0ULG6qtg                          NaN   \n",
       "-4RRsux7RIX19uBsVTSsMw                          NaN   \n",
       "-5E2DVKUmSqpKPOWAL7sTg                          NaN   \n",
       "-6t5bJFJne44N_E7lgdR6g                          NaN   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                          NaN   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                          NaN   \n",
       "-BCHgVhr-mx8iEBbphbTXw                          NaN   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                          NaN   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                          NaN   \n",
       "\n",
       "business_name           Alamo Drafthouse Cinema South Lamar  \\\n",
       "user_id                                                       \n",
       "-1CV3L7RAk34790wXVQu4g                                  NaN   \n",
       "-3s52C4zL_DHRK0ULG6qtg                                  NaN   \n",
       "-4RRsux7RIX19uBsVTSsMw                                  NaN   \n",
       "-5E2DVKUmSqpKPOWAL7sTg                                  NaN   \n",
       "-6t5bJFJne44N_E7lgdR6g                                  NaN   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                                  NaN   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                                  NaN   \n",
       "-BCHgVhr-mx8iEBbphbTXw                                  NaN   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                                  NaN   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                                  NaN   \n",
       "\n",
       "business_name           American Fresh Beer Garden  Anaheim Produce  \\\n",
       "user_id                                                               \n",
       "-1CV3L7RAk34790wXVQu4g                         NaN              NaN   \n",
       "-3s52C4zL_DHRK0ULG6qtg                         NaN              NaN   \n",
       "-4RRsux7RIX19uBsVTSsMw                         NaN              NaN   \n",
       "-5E2DVKUmSqpKPOWAL7sTg                         NaN              NaN   \n",
       "-6t5bJFJne44N_E7lgdR6g                         NaN              NaN   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                         NaN              NaN   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                         NaN              NaN   \n",
       "-BCHgVhr-mx8iEBbphbTXw                         NaN              NaN   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                         NaN              NaN   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                         NaN              NaN   \n",
       "\n",
       "business_name           Anna's Hand Cut Donuts  Arby's  Arepas Grill  ...  \\\n",
       "user_id                                                               ...   \n",
       "-1CV3L7RAk34790wXVQu4g                     NaN     NaN           NaN  ...   \n",
       "-3s52C4zL_DHRK0ULG6qtg                     NaN     NaN           NaN  ...   \n",
       "-4RRsux7RIX19uBsVTSsMw                     NaN     NaN           NaN  ...   \n",
       "-5E2DVKUmSqpKPOWAL7sTg                     NaN     NaN           NaN  ...   \n",
       "-6t5bJFJne44N_E7lgdR6g                     NaN     NaN           NaN  ...   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                     NaN     NaN           NaN  ...   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                     NaN     NaN           NaN  ...   \n",
       "-BCHgVhr-mx8iEBbphbTXw                     NaN     NaN           NaN  ...   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                     NaN     NaN           NaN  ...   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                     NaN     NaN           NaN  ...   \n",
       "\n",
       "business_name           Wok N' Kebab Asian Street Fare  X-Site Grill & Bistro  \\\n",
       "user_id                                                                         \n",
       "-1CV3L7RAk34790wXVQu4g                             NaN                    NaN   \n",
       "-3s52C4zL_DHRK0ULG6qtg                             NaN                    NaN   \n",
       "-4RRsux7RIX19uBsVTSsMw                             NaN                    NaN   \n",
       "-5E2DVKUmSqpKPOWAL7sTg                             NaN                    NaN   \n",
       "-6t5bJFJne44N_E7lgdR6g                             NaN                    NaN   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                             NaN                    NaN   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                             NaN                    NaN   \n",
       "-BCHgVhr-mx8iEBbphbTXw                             NaN                    NaN   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                             NaN                    NaN   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                             NaN                    NaN   \n",
       "\n",
       "business_name           Yan's China Bistro  Yoko Sushi   Yokohama Teppanyaki  \\\n",
       "user_id                                                                        \n",
       "-1CV3L7RAk34790wXVQu4g                 NaN          NaN                  NaN   \n",
       "-3s52C4zL_DHRK0ULG6qtg                 NaN          NaN                  NaN   \n",
       "-4RRsux7RIX19uBsVTSsMw                 NaN          NaN                  NaN   \n",
       "-5E2DVKUmSqpKPOWAL7sTg                 NaN          NaN                  NaN   \n",
       "-6t5bJFJne44N_E7lgdR6g                 NaN          NaN                  NaN   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                 NaN          NaN                  NaN   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                 NaN          NaN                  NaN   \n",
       "-BCHgVhr-mx8iEBbphbTXw                 NaN          NaN                  NaN   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                 NaN          NaN                  NaN   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                 NaN          NaN                  NaN   \n",
       "\n",
       "business_name           ZAZ  Zen Japanese Grill & Sushi Bar  \\\n",
       "user_id                                                       \n",
       "-1CV3L7RAk34790wXVQu4g  NaN                             NaN   \n",
       "-3s52C4zL_DHRK0ULG6qtg  NaN                             NaN   \n",
       "-4RRsux7RIX19uBsVTSsMw  NaN                             NaN   \n",
       "-5E2DVKUmSqpKPOWAL7sTg  NaN                             NaN   \n",
       "-6t5bJFJne44N_E7lgdR6g  NaN                             NaN   \n",
       "-9XyhOsyd8tOfgvFLGJfAQ  NaN                             NaN   \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ  NaN                             NaN   \n",
       "-BCHgVhr-mx8iEBbphbTXw  NaN                             NaN   \n",
       "-CUA8xL-9bCZN3nOi1e_Hw  NaN                             NaN   \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ  NaN                             NaN   \n",
       "\n",
       "business_name           Zeus Greek Street Food  Zoup!  sandoitchi Pop Up  \n",
       "user_id                                                                   \n",
       "-1CV3L7RAk34790wXVQu4g                     NaN    NaN                NaN  \n",
       "-3s52C4zL_DHRK0ULG6qtg                     NaN    NaN                NaN  \n",
       "-4RRsux7RIX19uBsVTSsMw                     NaN    NaN                NaN  \n",
       "-5E2DVKUmSqpKPOWAL7sTg                     NaN    NaN                NaN  \n",
       "-6t5bJFJne44N_E7lgdR6g                     NaN    NaN                NaN  \n",
       "-9XyhOsyd8tOfgvFLGJfAQ                     NaN    NaN                NaN  \n",
       "-A9ICz4e9hgrNK4-Fs3TmQ                     NaN    NaN                NaN  \n",
       "-BCHgVhr-mx8iEBbphbTXw                     NaN    NaN                NaN  \n",
       "-CUA8xL-9bCZN3nOi1e_Hw                     NaN    NaN                NaN  \n",
       "-CvJZ3v4XxnzQAIZu5JvxQ                     NaN    NaN                NaN  \n",
       "\n",
       "[10 rows x 365 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_crosstab.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3005, 365)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_crosstab.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is a great example for a **sparse matrix (sparse data).**\n",
    "- Meaning this matrix contains a lot of null (0) values. \n",
    "- **This is a common occurrence in ratings data, and an obstecle to recommender systems.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fill null values with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews_crosstab = reviews_crosstab.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Based Colleborative Filtering\n",
    "- **Transpose** reviews_crosstab so that restaurents(items) are in rows are users are columns\n",
    "- Identify optimal number of componenets for SVD\n",
    "- Run truncated SVD on User and Restaurant matrix (reviews_crosstab)\n",
    "- Calculate the pearson correlation to identify similar users. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import TruncatedSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Transpose the utility matrix (reviews_crosstab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3005, 365)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_crosstab.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert X values to float\n",
    "X = reviews_crosstab.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 3005)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = reviews_crosstab.T\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_id</th>\n",
       "      <th>-1CV3L7RAk34790wXVQu4g</th>\n",
       "      <th>-3s52C4zL_DHRK0ULG6qtg</th>\n",
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       "      <th>-CvJZ3v4XxnzQAIZu5JvxQ</th>\n",
       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>business_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&amp;pizza - Harvard Square</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19th Hole</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 Star Pizza</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>African Paradise Restaurant</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alamo Drafthouse Cinema South Lamar</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3005 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id                              -1CV3L7RAk34790wXVQu4g  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -3s52C4zL_DHRK0ULG6qtg  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -4RRsux7RIX19uBsVTSsMw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -5E2DVKUmSqpKPOWAL7sTg  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -6t5bJFJne44N_E7lgdR6g  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -9XyhOsyd8tOfgvFLGJfAQ  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -A9ICz4e9hgrNK4-Fs3TmQ  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -BCHgVhr-mx8iEBbphbTXw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -CUA8xL-9bCZN3nOi1e_Hw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              -CvJZ3v4XxnzQAIZu5JvxQ  ...  \\\n",
       "business_name                                                ...   \n",
       "&pizza - Harvard Square                                 0.0  ...   \n",
       "19th Hole                                               0.0  ...   \n",
       "5 Star Pizza                                            0.0  ...   \n",
       "African Paradise Restaurant                             0.0  ...   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0  ...   \n",
       "\n",
       "user_id                              zpwbkEZPuJ5kpYULdEuRiw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zrbk3Xzx6V3s4fCOvNBEKw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zs4NnxnKBKTCf-DlLXHyzg  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zsUMstOI2PIGkPsc58VZIw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              ztD-vr_Q2iBEw7g9qLRceQ  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zuJizFaUq59eywOjN5cdxw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zvTHuymw0LCHBcVEt5WHsA  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zwsfGSYBHnssi6mDYXepTw  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zxSTe_VEZPK8e2PnkwgcGA  \\\n",
       "business_name                                                 \n",
       "&pizza - Harvard Square                                 0.0   \n",
       "19th Hole                                               0.0   \n",
       "5 Star Pizza                                            0.0   \n",
       "African Paradise Restaurant                             0.0   \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0   \n",
       "\n",
       "user_id                              zzdi0RIbc21HMJYIpBg8lw  \n",
       "business_name                                                \n",
       "&pizza - Harvard Square                                 0.0  \n",
       "19th Hole                                               0.0  \n",
       "5 Star Pizza                                            0.0  \n",
       "African Paradise Restaurant                             0.0  \n",
       "Alamo Drafthouse Cinema South Lamar                     0.0  \n",
       "\n",
       "[5 rows x 3005 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Identify the optimal number of components for SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 3005)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "compSVD = TruncatedSVD(n_components=X.shape[1]-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TruncatedSVD(n_components=3004)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compSVD.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a list of explanied variances \n",
    "svd_var_ratios = compSVD.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create a function to get n_componenets that exceeds the defined threshold\n",
    "def select_n_components(var_ratio, goal_var: float) -> int:\n",
    "    # Set initial variance explained so far\n",
    "    total_variance = 0.0\n",
    "    \n",
    "    # Set initial number of features\n",
    "    n_components = 0\n",
    "    \n",
    "    # For the explained variance of each feature:\n",
    "    for explained_variance in var_ratio:\n",
    "        \n",
    "        # Add the explained variance to the total\n",
    "        total_variance += explained_variance\n",
    "        \n",
    "        # Add one to the number of components\n",
    "        n_components += 1\n",
    "        \n",
    "        # If we reach our goal level of explained variance\n",
    "        if total_variance >= goal_var:\n",
    "            # End the loop\n",
    "            break\n",
    "            \n",
    "    # Return the number of components\n",
    "    return n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the n_components using the function \n",
    "n_comp = select_n_components(svd_var_ratios, 0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "231"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07687188019966722"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_comp/X.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Run SVD using n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 3005)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 231)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVD = TruncatedSVD(n_components=n_comp, random_state=12)\n",
    "resultant_matrix = SVD.fit_transform(X)\n",
    "resultant_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Create correlation matrix using Pearson Correaltion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 365)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_mat = np.corrcoef(resultant_matrix)\n",
    "corr_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find indexes of restaurents to be recommended"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "business_name\n",
       "Lechon                                                 128\n",
       "Barcelona Wine Bar South End                            81\n",
       "Tin Shed Garden Cafe                                    64\n",
       "FLIP burger boutique                                    63\n",
       "Santarpio's Pizza                                       62\n",
       "JINYA Ramen Bar - Austin                                52\n",
       "J & M Diner                                             49\n",
       "H&F Burger                                              48\n",
       "Cooper's Hawk Winery & Restaurant - Waterford Lakes     47\n",
       "Sushi Katana                                            47\n",
       "Westgate Lakes Resort and Spa                           44\n",
       "Marutama Ramen                                          43\n",
       "Roaring Fork                                            42\n",
       "The COOP: A Southern Affair                             37\n",
       "Heo Eatery                                              37\n",
       "Shaking Crab - Newton                                   33\n",
       "Coquine                                                 31\n",
       "Caffè Nero                                              30\n",
       "Truva Turkish Kitchen                                   30\n",
       "Blaze Pizza                                             29\n",
       "Pammy's                                                 28\n",
       "Bluetop                                                 27\n",
       "Miller's Ale House - Orlando                            26\n",
       "Southland Whiskey Kitchen                               26\n",
       "Catch 122                                               25\n",
       "The Breakfast Club                                      25\n",
       "Carlo's Cucina Italiana                                 25\n",
       "Select Oyster Bar                                       25\n",
       "Maenam                                                  25\n",
       "Zeus Greek Street Food                                  24\n",
       "Parish Cafe and Bar                                     24\n",
       "Le's Vietnamese Resturant                               23\n",
       "Black Bear Diner                                        23\n",
       "Alamo Drafthouse Cinema South Lamar                     23\n",
       "Granny's Tacos                                          22\n",
       "City Barbeque                                           22\n",
       "Mattie's                                                21\n",
       "The Crepevine                                           21\n",
       "Five Horses Tavern                                      21\n",
       "Slim & Husky's Pizza Beeria                             20\n",
       "McMenamins on the Columbia                              20\n",
       "Tortas El Rey                                           20\n",
       "Locale                                                  19\n",
       "Boston Lobster Feast                                    19\n",
       "Rimsky-Korsakoffee House                                18\n",
       "Momo N Curry                                            18\n",
       "South Village Grille                                    17\n",
       "Red Robin Gourmet Burgers and Brews                     17\n",
       "Bhuna                                                   17\n",
       "Kyoto Sushi and Grill                                   16\n",
       "Bobby & Jack’s Memphis Barbecue                         16\n",
       "The One Sushi +                                         16\n",
       "Casa del Pollo                                          15\n",
       "Cilantro's Taqueria                                     15\n",
       "Champions                                               15\n",
       "Les Faux Bourgeois                                      15\n",
       "Sapporo Ramen                                           15\n",
       "BJ's Restaurant & Brewhouse                             15\n",
       "Bubble World                                            14\n",
       "Texican Cafe - Manchaca                                 14\n",
       "Johnny Carino's                                         13\n",
       "Wingstop                                                13\n",
       "Khob Khun Thai Cuisine                                  13\n",
       "Karver Sandwich Bar                                     13\n",
       "Tucci's                                                 13\n",
       "Java Cats Cafe                                          13\n",
       "Mark's Jamaican Bar & Grill                             13\n",
       "House of Blues Boston- Music Venue                      12\n",
       "Bakery Lorraine                                         12\n",
       "Wendy's                                                 12\n",
       "New Kings Buffet                                        12\n",
       "Arepazo Tapas & Wine                                    11\n",
       "Expatriate                                              11\n",
       "Martsa On Elm                                           11\n",
       "Zen Japanese Grill & Sushi Bar                          11\n",
       "Sissy K's                                               10\n",
       "Sorelle                                                 10\n",
       "Fuddruckers                                             10\n",
       "Ray's Fish & Chips                                      10\n",
       "License No. 1                                           10\n",
       "Pho Star in Vancouver                                   10\n",
       "Olive Garden Italian Restaurant                         10\n",
       "Guacamole Mexican Grill                                 10\n",
       "Marinepolis Sushi Land                                   9\n",
       "Jimmy Hula's                                             9\n",
       "Pio Pio Ocoee                                            9\n",
       "Kongee Dinesty                                           9\n",
       "Fado Pub & Kitchen                                       9\n",
       "Panera Bread                                             9\n",
       "Golden Swan Restaurant                                   9\n",
       "Koi Hot Pot                                              9\n",
       "Embilta Café & Restaurant Ethiopian Cuisine              9\n",
       "Olive Bistro Midtown                                     9\n",
       "LongHorn Steakhouse                                      9\n",
       "Vatica Indian Cuisine                                    9\n",
       "Burger King                                              9\n",
       "Chili's                                                  9\n",
       "Uptown Deli and Brew                                     9\n",
       "The Thirsty Topher                                       8\n",
       "Sulmida Dessert Cafe                                     8\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check which restaurent has most number of reviews \n",
    "reviewsData.groupby('business_name')['rating'].count().sort_values(ascending =False)[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Receommendations"
   ]
  },
  {
   "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>Correlation</th>\n",
       "      <th>Restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>FLIP burger boutique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>0.012086</td>\n",
       "      <td>sandoitchi Pop Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.008818</td>\n",
       "      <td>Sunny Street Cafe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.008106</td>\n",
       "      <td>Arepas Grill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0.008019</td>\n",
       "      <td>Yoko Sushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.007435</td>\n",
       "      <td>IHOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.007307</td>\n",
       "      <td>Hot Spot Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0.007076</td>\n",
       "      <td>Relish Burger Bistro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.007042</td>\n",
       "      <td>Pizza Peddler Deli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>0.006640</td>\n",
       "      <td>Waffle House</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Correlation            Restaurant\n",
       "102     1.000000  FLIP burger boutique\n",
       "364     0.012086     sandoitchi Pop Up\n",
       "287     0.008818     Sunny Street Cafe\n",
       "9       0.008106          Arepas Grill\n",
       "358     0.008019           Yoko Sushi \n",
       "139     0.007435                  IHOP\n",
       "135     0.007307        Hot Spot Pizza\n",
       "255     0.007076  Relish Burger Bistro\n",
       "243     0.007042    Pizza Peddler Deli\n",
       "346     0.006640          Waffle House"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_idx = reviews_crosstab.columns.get_loc(\"FLIP burger boutique\")\n",
    "specific_corr = corr_mat[col_idx]\n",
    "pd.DataFrame({'Correlation': specific_corr, \"Restaurant\":reviews_crosstab.columns}).sort_values(\n",
    "                                                                    'Correlation', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Correlation</th>\n",
       "      <th>Restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>Lechon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>0.012086</td>\n",
       "      <td>sandoitchi Pop Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.008818</td>\n",
       "      <td>Sunny Street Cafe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.008106</td>\n",
       "      <td>Arepas Grill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0.008019</td>\n",
       "      <td>Yoko Sushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.007435</td>\n",
       "      <td>IHOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.007307</td>\n",
       "      <td>Hot Spot Pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0.007076</td>\n",
       "      <td>Relish Burger Bistro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.007042</td>\n",
       "      <td>Pizza Peddler Deli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>0.006640</td>\n",
       "      <td>Waffle House</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Correlation            Restaurant\n",
       "172     1.000000                Lechon\n",
       "364     0.012086     sandoitchi Pop Up\n",
       "287     0.008818     Sunny Street Cafe\n",
       "9       0.008106          Arepas Grill\n",
       "358     0.008019           Yoko Sushi \n",
       "139     0.007435                  IHOP\n",
       "135     0.007307        Hot Spot Pizza\n",
       "255     0.007076  Relish Burger Bistro\n",
       "243     0.007042    Pizza Peddler Deli\n",
       "346     0.006640          Waffle House"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_idx = reviews_crosstab.columns.get_loc(\"Lechon\")\n",
    "specific_corr = corr_mat[col_idx]\n",
    "pd.DataFrame({'Correlation': specific_corr, \"Restaurant\":reviews_crosstab.columns}).sort_values(\n",
    "                                                                    'Correlation', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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_name</th>\n",
       "      <th>business_id</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>service_categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25749</th>\n",
       "      <td>Lechon</td>\n",
       "      <td>eUbq0uNxRlXQ6sy7phM7yA</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Tapas Bars, Restaurants, Cocktail Bars, Nightl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      business_name             business_id      city state  star_rating  \\\n",
       "25749        Lechon  eUbq0uNxRlXQ6sy7phM7yA  Portland    OR          4.5   \n",
       "\n",
       "                                      service_categories  \n",
       "25749  Tapas Bars, Restaurants, Cocktail Bars, Nightl...  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "business.loc[business.business_name == \"Lechon\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Correlation</th>\n",
       "      <th>Restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>Marutama Ramen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0.090809</td>\n",
       "      <td>Hong Sushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>0.012067</td>\n",
       "      <td>sandoitchi Pop Up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.009975</td>\n",
       "      <td>Sunny Street Cafe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0.009168</td>\n",
       "      <td>Yoko Sushi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.009054</td>\n",
       "      <td>IHOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.008537</td>\n",
       "      <td>Arepas Grill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.007979</td>\n",
       "      <td>Pizza Peddler Deli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.007490</td>\n",
       "      <td>Foodoko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>0.007384</td>\n",
       "      <td>Waffle House</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Correlation          Restaurant\n",
       "189     1.000000      Marutama Ramen\n",
       "134     0.090809          Hong Sushi\n",
       "364     0.012067   sandoitchi Pop Up\n",
       "287     0.009975   Sunny Street Cafe\n",
       "358     0.009168         Yoko Sushi \n",
       "139     0.009054                IHOP\n",
       "9       0.008537        Arepas Grill\n",
       "243     0.007979  Pizza Peddler Deli\n",
       "110     0.007490             Foodoko\n",
       "346     0.007384        Waffle House"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_idx = reviews_crosstab.columns.get_loc(\"Marutama Ramen\")\n",
    "specific_corr = corr_mat[col_idx]\n",
    "pd.DataFrame({'Correlation': specific_corr, \"Restaurant\":reviews_crosstab.columns}).sort_values(\n",
    "                                                                    'Correlation', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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