project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "df = pd.read_csv('./locations.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "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>place_id</th>\n",
       "      <th>name</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>user_ratings_total</th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ChIJ1e5UJJF0j1QR6VpPNf8TYiQ</td>\n",
       "      <td>Royal BC Museum</td>\n",
       "      <td>4.6</td>\n",
       "      <td>48.419811</td>\n",
       "      <td>-123.367450</td>\n",
       "      <td>8661</td>\n",
       "      <td>Great museum has very nice and extensive exhib...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ChIJmXc0Z6J0j1QR4CJUS73dyZk</td>\n",
       "      <td>Fisherman's Wharf Park</td>\n",
       "      <td>4.5</td>\n",
       "      <td>48.421950</td>\n",
       "      <td>-123.383014</td>\n",
       "      <td>7523</td>\n",
       "      <td>A popular tourist attraction on the Victoria w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ChIJueKTCmtzj1QRxJZTfhNWlsY</td>\n",
       "      <td>Esquimalt Gorge Park &amp; Pavilion</td>\n",
       "      <td>4.7</td>\n",
       "      <td>48.446345</td>\n",
       "      <td>-123.404949</td>\n",
       "      <td>1078</td>\n",
       "      <td>This park is so lovely! City staff do a great ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ChIJ5ZScPpF0j1QRBLCY8tTvWsM</td>\n",
       "      <td>Thunderbird Park</td>\n",
       "      <td>4.3</td>\n",
       "      <td>48.419794</td>\n",
       "      <td>-123.366680</td>\n",
       "      <td>203</td>\n",
       "      <td>Like the totems and of course worth visiting t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ChIJS9YYcJB0j1QRPWHIFtwTr18</td>\n",
       "      <td>Victoria Bug Zoo</td>\n",
       "      <td>4.5</td>\n",
       "      <td>48.423300</td>\n",
       "      <td>-123.366668</td>\n",
       "      <td>1822</td>\n",
       "      <td>My daughter and I had a fantastic time at the ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      place_id                             name  avg_rating  \\\n",
       "0  ChIJ1e5UJJF0j1QR6VpPNf8TYiQ                  Royal BC Museum         4.6   \n",
       "1  ChIJmXc0Z6J0j1QR4CJUS73dyZk           Fisherman's Wharf Park         4.5   \n",
       "2  ChIJueKTCmtzj1QRxJZTfhNWlsY  Esquimalt Gorge Park & Pavilion         4.7   \n",
       "3  ChIJ5ZScPpF0j1QRBLCY8tTvWsM                 Thunderbird Park         4.3   \n",
       "4  ChIJS9YYcJB0j1QRPWHIFtwTr18                 Victoria Bug Zoo         4.5   \n",
       "\n",
       "         lat         lng  user_ratings_total  \\\n",
       "0  48.419811 -123.367450                8661   \n",
       "1  48.421950 -123.383014                7523   \n",
       "2  48.446345 -123.404949                1078   \n",
       "3  48.419794 -123.366680                 203   \n",
       "4  48.423300 -123.366668                1822   \n",
       "\n",
       "                                             reviews  \n",
       "0  Great museum has very nice and extensive exhib...  \n",
       "1  A popular tourist attraction on the Victoria w...  \n",
       "2  This park is so lovely! City staff do a great ...  \n",
       "3  Like the totems and of course worth visiting t...  \n",
       "4  My daughter and I had a fantastic time at the ...  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(357, 7)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "place_id               0\n",
       "name                   0\n",
       "avg_rating             0\n",
       "lat                    0\n",
       "lng                    0\n",
       "user_ratings_total     0\n",
       "reviews               26\n",
       "dtype: int64"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at missing values as a total and a percentage\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the rows with missing values in reviews\n",
    "df.dropna(subset=['reviews'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at the distribution of user_ratings_total\n",
    "plt.hist(df['user_ratings_total'], bins=20)\n",
    "# add an x-axis label\n",
    "plt.xlabel('User Ratings Total')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the CDF of user_ratings_total as a line\n",
    "plt.plot(np.sort(df['user_ratings_total']), np.linspace(0, 1, len(df), endpoint=False))\n",
    "# plot the quartiles as red dashed lines\n",
    "plt.axvline(df['user_ratings_total'].quantile(0.25), color='red', linestyle='dashed', linewidth=1)\n",
    "plt.axvline(df['user_ratings_total'].quantile(0.5), color='red', linestyle='dashed', linewidth=1)\n",
    "plt.axvline(df['user_ratings_total'].quantile(0.75), color='red', linestyle='dashed', linewidth=1)\n",
    "\n",
    "# add an x-axis label\n",
    "plt.xlabel('User Ratings Total CDF')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the CDF of user_ratings_total as a line zoomed into 0-1000\n",
    "plt.plot(np.sort(df['user_ratings_total']), np.linspace(0, 1, len(df), endpoint=False))\n",
    "plt.xlim(0, 600)\n",
    "# plot the quartiles as red dashed lines with a label for each quartile\n",
    "plt.axvline(df['user_ratings_total'].quantile(0.25), color='red', linestyle='dashed', linewidth=1, label='Q1')\n",
    "plt.axvline(df['user_ratings_total'].quantile(0.5), color='red', linestyle='dashed', linewidth=1, label='Q2')\n",
    "plt.axvline(df['user_ratings_total'].quantile(0.75), color='red', linestyle='dashed', linewidth=1, label='Q3')\n",
    "\n",
    "# adding fill\n",
    "\n",
    "# fill between 0 and Q1\n",
    "plt.fill_between([0, df['user_ratings_total'].quantile(0.25)], 0, 1, alpha=0.3, color='red')\n",
    "\n",
    "# fill between Q1 and Q2\n",
    "plt.fill_between([df['user_ratings_total'].quantile(0.25), df['user_ratings_total'].quantile(0.5)], 0, 1, alpha=0.3, color='orange')\n",
    "\n",
    "# fill between Q2 and Q3\n",
    "plt.fill_between([df['user_ratings_total'].quantile(0.5), df['user_ratings_total'].quantile(0.75)], 0, 1, alpha=0.3, color='green')\n",
    "\n",
    "# fill between Q3 and max\n",
    "plt.fill_between([df['user_ratings_total'].quantile(0.75), df['user_ratings_total'].max()], 0, 1, alpha=0.3, color='blue')\n",
    "\n",
    "# add the numerical values of the quartiles on the x-axis\n",
    "Q1 = df['user_ratings_total'].quantile(0.25)\n",
    "Q2 = df['user_ratings_total'].quantile(0.5)\n",
    "Q3 = df['user_ratings_total'].quantile(0.75)\n",
    "\n",
    "plt.xticks([Q1, Q2, Q3, 600], [Q1, Q2, Q3, \"600\"])\n",
    "\n",
    "plt.xlabel('User Ratings Total CDF (zoomed)')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Originally we were going use the average user rating as a feature, but after seeing the skewed distribution of the number of reviews we had to come up with a feature which also took into account how many user reveiws there were to more accurately score the locations."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Bayesian View"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.johndcook.com/blog/2011/09/27/bayesian-amazon/\n",
    "\n",
    "![Probability Liking](./probabilitiy_liking.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q: Which reseller is likely to give the best service?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Thanks to this video by 3blue1brown*\n",
    "\n",
    "https://www.youtube.com/watch?v=8idr1WZ1A7Q"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A: Pretend that there are two more reviews, one positive and one negative, for each reseller. The resulting ratio is your probability of having a good experience with that reseller."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./prob_liking_graphic.png\" width=\"400\"/>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this information we can create a feature for each location as the probability of having a good experience based on the average rating and the total number of ratings."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the \"probability of enjoying\" feature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column in the df dataframe called prob_enjoy\n",
    "\n",
    "positive_percentage = df['avg_rating'] / 5\n",
    "num_positive_reviews = df['user_ratings_total'] * positive_percentage\n",
    "\n",
    "df['prob_enjoy'] = (num_positive_reviews + 1) / (df['user_ratings_total'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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>name</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>user_ratings_total</th>\n",
       "      <th>prob_enjoy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>A Taste of Victoria Food Tours</td>\n",
       "      <td>5.0</td>\n",
       "      <td>157</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Bungay's Bespoke Bicycle Service</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>Mama Nadia's Kitchen</td>\n",
       "      <td>5.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Bishop's Family Cycles</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Finlayson Point Beach</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rainforest Tours</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Bagheera Boutique</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>The Breakwater</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>view of Victoria Harbour</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Old Victoria Customs House</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 name  avg_rating  user_ratings_total  \\\n",
       "53     A Taste of Victoria Food Tours         5.0                 157   \n",
       "347  Bungay's Bespoke Bicycle Service         5.0                 111   \n",
       "272              Mama Nadia's Kitchen         5.0                  74   \n",
       "336            Bishop's Family Cycles         5.0                  29   \n",
       "104             Finlayson Point Beach         5.0                  11   \n",
       "29                   Rainforest Tours         5.0                  10   \n",
       "131                 Bagheera Boutique         5.0                   9   \n",
       "45                     The Breakwater         5.0                   8   \n",
       "67           view of Victoria Harbour         5.0                   6   \n",
       "72         Old Victoria Customs House         5.0                   6   \n",
       "\n",
       "     prob_enjoy  \n",
       "53          1.0  \n",
       "347         1.0  \n",
       "272         1.0  \n",
       "336         1.0  \n",
       "104         1.0  \n",
       "29          1.0  \n",
       "131         1.0  \n",
       "45          1.0  \n",
       "67          1.0  \n",
       "72          1.0  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort according to prob_enjoy\n",
    "df[[\"name\", \"avg_rating\", \"user_ratings_total\", \"prob_enjoy\"]].sort_values(by=['prob_enjoy', 'user_ratings_total'], ascending=False, inplace=False).head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>00</th>\n",
       "      <th>10</th>\n",
       "      <th>100</th>\n",
       "      <th>1000</th>\n",
       "      <th>10pm</th>\n",
       "      <th>10th</th>\n",
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       "      <th>1130pm</th>\n",
       "      <th>11am</th>\n",
       "      <th>11pm</th>\n",
       "      <th>...</th>\n",
       "      <th>zambri</th>\n",
       "      <th>zambris</th>\n",
       "      <th>zara</th>\n",
       "      <th>zero</th>\n",
       "      <th>zesty</th>\n",
       "      <th>zip</th>\n",
       "      <th>zipline</th>\n",
       "      <th>zone</th>\n",
       "      <th>zoo</th>\n",
       "      <th>zoom</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 7233 columns</p>\n",
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      "text/plain": [
       "         00   10  100  1000      10pm  10th   11  1130pm  11am  11pm  ...  \\\n",
       "0  0.068927  0.0  0.0   0.0  0.095589   0.0  0.0     0.0   0.0   0.0  ...   \n",
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       "2  0.000000  0.0  0.0   0.0  0.000000   0.0  0.0     0.0   0.0   0.0  ...   \n",
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       "4  0.000000  0.0  0.0   0.0  0.000000   0.0  0.0     0.0   0.0   0.0  ...   \n",
       "\n",
       "   zambri  zambris  zara  zero  zesty  zip  zipline  zone       zoo  zoom  \n",
       "0     0.0      0.0   0.0   0.0    0.0  0.0      0.0   0.0  0.000000   0.0  \n",
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       "4     0.0      0.0   0.0   0.0    0.0  0.0      0.0   0.0  0.336469   0.0  \n",
       "\n",
       "[5 rows x 7233 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "# Compute TF-IDF values\n",
    "vectorizer = TfidfVectorizer(stop_words='english')\n",
    "tfidf_matrix = vectorizer.fit_transform(df['reviews'])\n",
    "\n",
    "# Create a DataFrame with these features\n",
    "feature_names = vectorizer.get_feature_names_out()\n",
    "df_tfidf = pd.DataFrame(tfidf_matrix.toarray(), columns=feature_names)\n",
    "\n",
    "df_tfidf.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Recommendation Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the cosine similarity between the user_input and all of the reviews\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import regex as re\n",
    "\n",
    "def make_recomendation(user_input):\n",
    "    # Compute TF-IDF values\n",
    "    user_input_prepared = vectorizer.transform([user_input])\n",
    "    # Find the cosine similarity between the user_input and all of the reviews\n",
    "    cosine_similarities = cosine_similarity(user_input_prepared, tfidf_matrix).flatten()\n",
    "    # scale the cosine_similarities by the prob_enjoy column\n",
    "    cosine_similarities = cosine_similarities * df['prob_enjoy']\n",
    "    # Find the index of the top 5 most similar locations\n",
    "    related_locations_indices = cosine_similarities.argsort()[:-6:-1]\n",
    "    # Print the top 5 most similar locations\n",
    "    return df.iloc[related_locations_indices]\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making a Recommendation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_input = \"I love going to a beach or a park for a walk at sunset. ideally at a place with great views. I also enjoy live music or any kind of theatre where there is a performance happening. I also love to go to museums and learn about the history of the place I'm visiting. My favourite food is Italian and I love eating anywhere with a great staff and proffesional service.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove punctuation\n",
    "pattern = r\"[,.]\"\n",
    "user_input = re.sub(pattern, \"\", user_input)\n",
    "\n",
    "user_input_no_stop = ' '.join([word for word in user_input.split() if word not in vectorizer.get_stop_words()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "recommended = make_recomendation(user_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highrock Cairn Park\n",
      "------------------\n",
      "\u001b[90mGreat\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mto\u001b[0m \u001b[90mtake\u001b[0m \u001b[90myour\u001b[0m \u001b[90mdog\u001b[0m \u001b[90mto\u001b[0m \u001b[90mrun\u001b[0m \u001b[90maround\u001b[0m \u001b[90min\u001b[0m \u001b[90ma\u001b[0m \u001b[90msafe\u001b[0m \u001b[90menvironment!\u001b[0m \u001b[90mFantastic\u001b[0m \u001b[90mview\u001b[0m \u001b[90mof\u001b[0m \u001b[90mVictoria.\u001b[0m\n",
      "\u001b[90mNatural\u001b[0m \u001b[90mbeauty\u001b[0m \u001b[90mfun\u001b[0m \u001b[90mlittle\u001b[0m \u001b[31mwalk\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mbeautiful\u001b[0m \u001b[31mviews\u001b[0m \u001b[90mfor\u001b[0m \u001b[90msunset!\u001b[0m \u001b[90mGreat\u001b[0m \u001b[31mviews\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[90mnice\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mto\u001b[0m\n",
      "\u001b[90msee\u001b[0m \u001b[90ma\u001b[0m \u001b[31msunset\u001b[0m \u001b[90monce\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[90mwhile.\u001b[0m \u001b[90mGood\u001b[0m \u001b[90mlittle\u001b[0m \u001b[90mjourney\u001b[0m \u001b[90mto\u001b[0m \u001b[90mdowntown\u001b[0m \u001b[90mbut\u001b[0m \u001b[90mworth\u001b[0m \u001b[90mthe\u001b[0m \u001b[31mviews\u001b[0m \u001b[90m.\u001b[0m\n",
      "\u001b[90mFavourite\u001b[0m \u001b[90mchildhood\u001b[0m \u001b[90mpark.\u001b[0m \u001b[90mGreat\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mto\u001b[0m \u001b[31mwalk\u001b[0m \u001b[90myour\u001b[0m \u001b[90mdog\u001b[0m \u001b[90mor\u001b[0m \u001b[90mhang\u001b[0m \u001b[90mout\u001b[0m \u001b[90mand\u001b[0m \u001b[90mread\u001b[0m \u001b[90ma\u001b[0m \u001b[90mbook.\u001b[0m \u001b[90mHas\u001b[0m\n",
      "\u001b[90ma\u001b[0m \u001b[31mgreat\u001b[0m \u001b[90mview\u001b[0m \u001b[90mof\u001b[0m \u001b[90mVictoria\u001b[0m \u001b[90mand\u001b[0m \u001b[90mEsquimalt\u001b[0m \u001b[90mfrom\u001b[0m \u001b[90mthe\u001b[0m \u001b[90msummit\u001b[0m\n",
      "\n",
      "\n",
      "II Terrazzo\n",
      "------------------\n",
      "\u001b[90mWe\u001b[0m \u001b[90mwent\u001b[0m \u001b[90mthere\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mmy\u001b[0m \u001b[90mbirthday.\u001b[0m \u001b[90mThe\u001b[0m \u001b[90matmosphere\u001b[0m \u001b[90mis\u001b[0m \u001b[90mfantastic.\u001b[0m \u001b[90mGreat\u001b[0m \u001b[90mrestaurant\u001b[0m \u001b[90mand\u001b[0m \u001b[90moutstanding\u001b[0m \u001b[90mservice.\u001b[0m \u001b[90mWe\u001b[0m\n",
      "\u001b[31mlove\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mmussels,\u001b[0m \u001b[90mand\u001b[0m \u001b[90mit's\u001b[0m \u001b[90mjuicy\u001b[0m \u001b[90mand\u001b[0m \u001b[90mfresh.\u001b[0m \u001b[90mJust\u001b[0m \u001b[90ma\u001b[0m \u001b[90mlittle\u001b[0m \u001b[90mbit\u001b[0m \u001b[90mdisappointed\u001b[0m \u001b[90mabout\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mlamb\u001b[0m \u001b[90mwe\u001b[0m\n",
      "\u001b[90mordered.\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mlamb\u001b[0m \u001b[90mquality\u001b[0m \u001b[90mis\u001b[0m \u001b[90mexcellent,\u001b[0m \u001b[90mbut\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mstyle\u001b[0m \u001b[90mof\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mdishes\u001b[0m \u001b[90mis\u001b[0m \u001b[90mnot\u001b[0m \u001b[90mour\u001b[0m \u001b[90mtype.\u001b[0m \u001b[90mReally\u001b[0m\n",
      "\u001b[90menjoyed\u001b[0m \u001b[90mthe\u001b[0m \u001b[31mfood\u001b[0m \u001b[90mand\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mservice.\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mportions\u001b[0m \u001b[90mwere\u001b[0m \u001b[90mgenerous.\u001b[0m \u001b[90mDefinitely\u001b[0m \u001b[90ma\u001b[0m \u001b[90mmust\u001b[0m \u001b[90mgo\u001b[0m \u001b[90mif\u001b[0m \u001b[90myou\u001b[0m \u001b[90mare\u001b[0m\n",
      "\u001b[90min\u001b[0m \u001b[90mVictoria\u001b[0m \u001b[90mIncredible\u001b[0m \u001b[31mItalian\u001b[0m \u001b[31mfood\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mlarge\u001b[0m \u001b[90mportions\u001b[0m \u001b[90mand\u001b[0m \u001b[90msuper\u001b[0m \u001b[90mfriendly\u001b[0m \u001b[90mstaff.\u001b[0m \u001b[90mReally\u001b[0m \u001b[90mfun\u001b[0m \u001b[90matmosphere\u001b[0m\n",
      "\u001b[90minside\u001b[0m \u001b[90mas\u001b[0m \u001b[90mwell.\u001b[0m \u001b[90mEasily\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mbest\u001b[0m \u001b[90mmeal\u001b[0m \u001b[90mwe\u001b[0m \u001b[90mhad\u001b[0m \u001b[90mduring\u001b[0m \u001b[90mour\u001b[0m \u001b[90mtrip\u001b[0m \u001b[90mto\u001b[0m \u001b[90mVictoria.\u001b[0m \u001b[90mEasily\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mbest\u001b[0m\n",
      "\u001b[90mand\u001b[0m \u001b[90mmost\u001b[0m \u001b[90mauthentic\u001b[0m \u001b[31mItalian\u001b[0m \u001b[90mcuisine\u001b[0m \u001b[90mon\u001b[0m \u001b[90mthe\u001b[0m \u001b[90misland.\u001b[0m \u001b[90mFantastic\u001b[0m \u001b[90mmenu,\u001b[0m \u001b[90mfabulous\u001b[0m \u001b[90mservice,\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[90mtruly\u001b[0m \u001b[90mincredible\u001b[0m\n",
      "\u001b[90mwine\u001b[0m \u001b[90mselection.\u001b[0m \u001b[31mI\u001b[0m \u001b[90mrecommend\u001b[0m \u001b[90mthis\u001b[0m \u001b[90mrestaurant\u001b[0m \u001b[90mto\u001b[0m \u001b[90manyone\u001b[0m \u001b[31mvisiting\u001b[0m \u001b[90mVictoria.\u001b[0m \u001b[90mWe\u001b[0m \u001b[90mhave\u001b[0m \u001b[90mbeen\u001b[0m \u001b[90mmany\u001b[0m \u001b[90mtimes\u001b[0m \u001b[90mand\u001b[0m\n",
      "\u001b[90malways\u001b[0m \u001b[90mhave\u001b[0m \u001b[90ma\u001b[0m \u001b[31mgreat\u001b[0m \u001b[90mexperience\u001b[0m \u001b[90malongside\u001b[0m \u001b[90mfriendly\u001b[0m \u001b[90mand\u001b[0m \u001b[90mattentive\u001b[0m \u001b[31mservice\u001b[0m \u001b[90mIt’s\u001b[0m \u001b[90ma\u001b[0m \u001b[31mgreat\u001b[0m \u001b[90mrestaurant.\u001b[0m \u001b[90mIt’s\u001b[0m\n",
      "\u001b[90mromantic\u001b[0m \u001b[90mand\u001b[0m \u001b[90mcozy.\u001b[0m \u001b[90mAlso\u001b[0m \u001b[90mVery\u001b[0m \u001b[90myummy.\u001b[0m \u001b[90mPortions\u001b[0m \u001b[90mare\u001b[0m \u001b[90mhuge.\u001b[0m \u001b[90mMake\u001b[0m \u001b[90mreservations\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mtime.\u001b[0m \u001b[90mI’ll\u001b[0m \u001b[90mdefinitely\u001b[0m\n",
      "\u001b[90mrecommend\u001b[0m \u001b[90mthis\u001b[0m \u001b[90mrestaurant.\u001b[0m \u001b[90mHowever\u001b[0m \u001b[31mI\u001b[0m \u001b[90mdid\u001b[0m \u001b[90mmiss\u001b[0m \u001b[90mfrom\u001b[0m \u001b[90mtheir\u001b[0m \u001b[90mmenu\u001b[0m \u001b[90ma\u001b[0m \u001b[90mmore\u001b[0m \u001b[31mItalian\u001b[0m \u001b[90mstyle\u001b[0m \u001b[90mpasta.\u001b[0m\n",
      "\n",
      "\n",
      "Macaulay Point Park\n",
      "------------------\n",
      "\u001b[90mYou\u001b[0m \u001b[90mabsolutely\u001b[0m \u001b[90mcan't\u001b[0m \u001b[90mloose\u001b[0m \u001b[90mvisiting.\u001b[0m \u001b[90mNice\u001b[0m \u001b[90mocean\u001b[0m \u001b[31mviews\u001b[0m \u001b[90mand\u001b[0m \u001b[31mhistory\u001b[0m \u001b[90mof\u001b[0m \u001b[90mCanada's\u001b[0m \u001b[90mdefences.\u001b[0m \u001b[90mWith\u001b[0m \u001b[90mold\u001b[0m \u001b[90mmilitary\u001b[0m\n",
      "\u001b[90mramparts\u001b[0m \u001b[90mand\u001b[0m \u001b[90meven\u001b[0m \u001b[90ma\u001b[0m \u001b[90mtunnel\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mold\u001b[0m \u001b[90mand\u001b[0m \u001b[90myoung\u001b[0m \u001b[90mtoo\u001b[0m \u001b[90mexplore.\u001b[0m \u001b[90mThis\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mis\u001b[0m \u001b[90mwell\u001b[0m \u001b[90mworth\u001b[0m \u001b[90mthe\u001b[0m\n",
      "\u001b[90mvisit.\u001b[0m \u001b[90mWe’ll\u001b[0m \u001b[90mgroomed\u001b[0m \u001b[90mtrails\u001b[0m \u001b[31mwalk\u001b[0m \u001b[90myou\u001b[0m \u001b[90mthrough\u001b[0m \u001b[90ma\u001b[0m \u001b[90mhistoric\u001b[0m \u001b[90mmilitary\u001b[0m \u001b[90msite\u001b[0m \u001b[90mwith\u001b[0m \u001b[90minterpretive\u001b[0m \u001b[90minformation\u001b[0m \u001b[90mand\u001b[0m\n",
      "\u001b[31mgreat\u001b[0m \u001b[31mviews\u001b[0m \u001b[90mor\u001b[0m \u001b[90mVictoria\u001b[0m \u001b[90mand\u001b[0m \u001b[90mtowards\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mOlympic\u001b[0m \u001b[90mMountains.\u001b[0m \u001b[90mThis\u001b[0m \u001b[90mis\u001b[0m \u001b[90ma\u001b[0m \u001b[90mhigh\u001b[0m \u001b[90mcalibre\u001b[0m \u001b[90m(no\u001b[0m \u001b[90mpun\u001b[0m\n",
      "\u001b[90mintended)\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mfree\u001b[0m \u001b[90mparking\u001b[0m \u001b[90mand\u001b[0m \u001b[90mno\u001b[0m \u001b[90mentry\u001b[0m \u001b[90mfee.\u001b[0m \u001b[90mIt’s\u001b[0m \u001b[90mdog\u001b[0m \u001b[90mfriendly…\u001b[0m \u001b[90ma\u001b[0m \u001b[90mreal\u001b[0m \u001b[90mlocal\u001b[0m \u001b[90mgem.\u001b[0m \u001b[90mA\u001b[0m\n",
      "\u001b[90mnice\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mfor\u001b[0m \u001b[90ma\u001b[0m \u001b[90mquick\u001b[0m \u001b[31mwalk\u001b[0m \u001b[90malong\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mwater,\u001b[0m \u001b[90msome\u001b[0m \u001b[90mhistoric\u001b[0m \u001b[90msites\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[90mbeautiful\u001b[0m \u001b[90mview\u001b[0m \u001b[90mof\u001b[0m\n",
      "\u001b[90mthe\u001b[0m \u001b[90mmountains!\u001b[0m \u001b[90mWe\u001b[0m \u001b[90mstayed\u001b[0m \u001b[90min\u001b[0m \u001b[90man\u001b[0m \u001b[90mAirBnB\u001b[0m \u001b[90mright\u001b[0m \u001b[90mat\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mentrance\u001b[0m \u001b[90mof\u001b[0m \u001b[90mthis\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mand\u001b[0m \u001b[90mit\u001b[0m \u001b[90mwas\u001b[0m\n",
      "\u001b[90mstunning!\u001b[0m \u001b[90mYou\u001b[0m \u001b[90mcan\u001b[0m \u001b[90mwatch\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mcruise\u001b[0m \u001b[90mships\u001b[0m \u001b[90mcome\u001b[0m \u001b[90mand\u001b[0m \u001b[90mgo,\u001b[0m \u001b[90mas\u001b[0m \u001b[90mwell\u001b[0m \u001b[90mas\u001b[0m \u001b[90mexplore\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mold\u001b[0m \u001b[90mbarracks.\u001b[0m\n",
      "\u001b[90mThere\u001b[0m \u001b[90mis\u001b[0m \u001b[90mwildlife\u001b[0m \u001b[90mto\u001b[0m \u001b[90mwatch,\u001b[0m \u001b[90mand\u001b[0m \u001b[90mvery\u001b[0m \u001b[90mfriendly\u001b[0m \u001b[90mlocals\u001b[0m \u001b[90mwalking\u001b[0m \u001b[90mtheir\u001b[0m \u001b[90mdogs.\u001b[0m \u001b[90mFabulous\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mthat\u001b[0m \u001b[90mwe\u001b[0m\n",
      "\u001b[90malmost\u001b[0m \u001b[90mmissed\u001b[0m \u001b[90mout\u001b[0m \u001b[90mon.\u001b[0m \u001b[90mWe\u001b[0m \u001b[90mwere\u001b[0m \u001b[90mconfused\u001b[0m \u001b[90mby\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mlocked\u001b[0m \u001b[90mgate\u001b[0m \u001b[90mand\u001b[0m \u001b[90mno\u001b[0m \u001b[90mparking\u001b[0m \u001b[90msigns\u001b[0m \u001b[90mevery\u001b[0m \u001b[90mwhere\u001b[0m\n",
      "\u001b[90mnear\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mentrance.\u001b[0m \u001b[90mGlad\u001b[0m \u001b[90mwe\u001b[0m \u001b[90mtook\u001b[0m \u001b[90ma\u001b[0m \u001b[90mchance\u001b[0m \u001b[90mand\u001b[0m \u001b[90mparked\u001b[0m \u001b[90mfurther\u001b[0m \u001b[90mdown\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mstreet.\u001b[0m \u001b[90mThe\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mhad\u001b[0m\n",
      "\u001b[90mlots\u001b[0m \u001b[90mof\u001b[0m \u001b[90mpathways\u001b[0m \u001b[90mleading\u001b[0m \u001b[90mto\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mwater\u001b[0m \u001b[90mand\u001b[0m \u001b[90malong\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mwater.\u001b[0m \u001b[90mThis\u001b[0m \u001b[90mis\u001b[0m \u001b[90ma\u001b[0m \u001b[31mgreat\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mto\u001b[0m \u001b[31mwalk\u001b[0m\n",
      "\u001b[90myour\u001b[0m \u001b[90mdog\u001b[0m \u001b[90mand/or\u001b[0m \u001b[31menjoy\u001b[0m \u001b[31mgreat\u001b[0m \u001b[31mviews\u001b[0m \u001b[90mof\u001b[0m \u001b[90mJuan\u001b[0m \u001b[90mde\u001b[0m \u001b[90mfuca\u001b[0m \u001b[90mstraight.\u001b[0m \u001b[90mThere\u001b[0m \u001b[90mare\u001b[0m \u001b[90mbenches\u001b[0m \u001b[90malong\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mlook\u001b[0m\n",
      "\u001b[90mouts\u001b[0m \u001b[90mand\u001b[0m \u001b[90meven\u001b[0m \u001b[90ma\u001b[0m \u001b[90mpicnic\u001b[0m \u001b[90marea.\u001b[0m \u001b[90mThe\u001b[0m \u001b[31mpark\u001b[0m \u001b[90msigns\u001b[0m \u001b[90msay\u001b[0m \u001b[90mthe\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mis\u001b[0m \u001b[90monly\u001b[0m \u001b[90mopen\u001b[0m \u001b[90mfrom\u001b[0m \u001b[90mdawn\u001b[0m \u001b[90mto\u001b[0m\n",
      "\u001b[90mdusk\u001b[0m \u001b[90mhowever.\u001b[0m \u001b[90mOverall\u001b[0m \u001b[90ma\u001b[0m \u001b[90mreally\u001b[0m \u001b[90mnice\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mwell\u001b[0m \u001b[90mworth\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mvisit.\u001b[0m\n",
      "\n",
      "\n",
      "Cadboro-Gyro Park\n",
      "------------------\n",
      "\u001b[90mAmazing\u001b[0m \u001b[90mbeachfront\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mwith\u001b[0m \u001b[90msoft\u001b[0m \u001b[90msand\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[31mgreat\u001b[0m \u001b[90mplayground.\u001b[0m \u001b[90mPerfect\u001b[0m \u001b[90mfor\u001b[0m \u001b[90ma\u001b[0m \u001b[90mpicnic\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mseveral\u001b[0m\n",
      "\u001b[90mtables.\u001b[0m \u001b[90mAn\u001b[0m \u001b[90mideal\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mfamilies\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mkids.\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mplayground\u001b[0m \u001b[90mhas\u001b[0m \u001b[90mswings\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mlittle\u001b[0m \u001b[90mkids\u001b[0m \u001b[90mand\u001b[0m \u001b[90mbig\u001b[0m\n",
      "\u001b[90mkids,\u001b[0m \u001b[90ma\u001b[0m \u001b[90mfew\u001b[0m \u001b[90mclimbable\u001b[0m \u001b[90msea\u001b[0m \u001b[90mmonsters\u001b[0m \u001b[90mand\u001b[0m \u001b[90msea\u001b[0m \u001b[90mcreatures,\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[31mgreat\u001b[0m \u001b[90mpirate\u001b[0m \u001b[90mship\u001b[0m \u001b[90mwith\u001b[0m \u001b[90ma\u001b[0m \u001b[90mplank!\u001b[0m\n",
      "\u001b[90mMost\u001b[0m \u001b[90mimportantly,\u001b[0m \u001b[90mthere\u001b[0m \u001b[90mis\u001b[0m \u001b[90ma\u001b[0m \u001b[90mwashroom\u001b[0m \u001b[90mright\u001b[0m \u001b[90mnext\u001b[0m \u001b[90mto\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mplay\u001b[0m \u001b[90marea\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mwater\u001b[0m \u001b[90mbottle\u001b[0m \u001b[90mrefill\u001b[0m\n",
      "\u001b[90mstation\u001b[0m \u001b[90mand\u001b[0m \u001b[90mrinse-off\u001b[0m \u001b[90mstation\u001b[0m \u001b[90mfor\u001b[0m \u001b[90msandy\u001b[0m \u001b[90mlittle\u001b[0m \u001b[90mtoes.\u001b[0m \u001b[90mOur\u001b[0m \u001b[90mson\u001b[0m \u001b[90mloved\u001b[0m \u001b[90mthis\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mand\u001b[0m \u001b[90mso\u001b[0m \u001b[90mdid\u001b[0m \u001b[90mwe!\u001b[0m\n",
      "\u001b[90mOne\u001b[0m \u001b[90mof\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mbest\u001b[0m \u001b[90mplaygrounds\u001b[0m \u001b[90mI’ve\u001b[0m \u001b[90mbeen\u001b[0m \u001b[90mto!\u001b[0m \u001b[90mOctopus\u001b[0m \u001b[90mslides,\u001b[0m \u001b[90mwhale\u001b[0m \u001b[90mand\u001b[0m \u001b[90msea\u001b[0m \u001b[90mserpent\u001b[0m \u001b[90mclimbing\u001b[0m \u001b[90mstructures,\u001b[0m\n",
      "\u001b[90mand\u001b[0m \u001b[90mmany\u001b[0m \u001b[90mmore\u001b[0m \u001b[90mocean-themed\u001b[0m \u001b[90moriginal\u001b[0m \u001b[90mcreations.\u001b[0m \u001b[90mThe\u001b[0m \u001b[31mbeach\u001b[0m \u001b[90mitself\u001b[0m \u001b[90mis\u001b[0m \u001b[90msandy\u001b[0m \u001b[90mand\u001b[0m \u001b[90mclean.\u001b[0m \u001b[90mWhat\u001b[0m \u001b[90ma\u001b[0m \u001b[90mbeautiful\u001b[0m\n",
      "\u001b[90mpark!\u001b[0m \u001b[90mBeautiful\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mand\u001b[0m \u001b[90mbeach.\u001b[0m \u001b[90mIt\u001b[0m \u001b[90mhas\u001b[0m \u001b[90meverything\u001b[0m \u001b[90mthat\u001b[0m \u001b[90ma\u001b[0m \u001b[90mfamily\u001b[0m \u001b[90mcan\u001b[0m \u001b[90menjoy.\u001b[0m \u001b[31mI\u001b[0m \u001b[90mcan\u001b[0m \u001b[90mspend\u001b[0m \u001b[90ma\u001b[0m\n",
      "\u001b[90mwhole\u001b[0m \u001b[90mday\u001b[0m \u001b[90min\u001b[0m \u001b[90mthat\u001b[0m \u001b[90mpark.\u001b[0m \u001b[90mLove\u001b[0m \u001b[90mthat\u001b[0m \u001b[90mthey\u001b[0m \u001b[90malso\u001b[0m \u001b[90mhave\u001b[0m \u001b[90mzipline.\u001b[0m \u001b[90mOur\u001b[0m \u001b[31mfavourite\u001b[0m \u001b[31mbeach\u001b[0m \u001b[90mand\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mto\u001b[0m\n",
      "\u001b[90mvisit\u001b[0m \u001b[90mwhen\u001b[0m \u001b[90min\u001b[0m \u001b[90mVictoria.\u001b[0m \u001b[90mWhile\u001b[0m \u001b[90mbusy\u001b[0m \u001b[90mat\u001b[0m \u001b[90mtimes,\u001b[0m \u001b[90mthe\u001b[0m \u001b[31mbeach\u001b[0m \u001b[90mhas\u001b[0m \u001b[90ma\u001b[0m \u001b[90mtranquil\u001b[0m \u001b[90mvibe.\u001b[0m \u001b[90mGreat\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mkids\u001b[0m\n",
      "\u001b[90mand\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mchill\u001b[0m \u001b[90mget-togethers\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mfriends\u001b[0m \u001b[90mand\u001b[0m \u001b[90mfamily.\u001b[0m \u001b[90mCadboro\u001b[0m \u001b[90mGyro\u001b[0m \u001b[90mPark\u001b[0m \u001b[90mis\u001b[0m \u001b[90ma\u001b[0m \u001b[31mgreat\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mto\u001b[0m \u001b[90mvisit\u001b[0m\n",
      "\u001b[90min\u001b[0m \u001b[90mVictoria,\u001b[0m \u001b[90mBritish\u001b[0m \u001b[90mColumbia.\u001b[0m \u001b[90mIt\u001b[0m \u001b[90mhas\u001b[0m \u001b[90ma\u001b[0m \u001b[90mbeautiful\u001b[0m \u001b[31mbeach\u001b[0m \u001b[90mwith\u001b[0m \u001b[90msoft\u001b[0m \u001b[90msand\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[90msection\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mdogs\u001b[0m\n",
      "\u001b[90mand\u001b[0m \u001b[90manother\u001b[0m \u001b[90msection\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mkids\u001b[0m \u001b[90mto\u001b[0m \u001b[90mplay\u001b[0m \u001b[90mwithout\u001b[0m \u001b[90mdogs.\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mplayground\u001b[0m \u001b[90mis\u001b[0m \u001b[90mreally\u001b[0m \u001b[90mfun\u001b[0m \u001b[90mand\u001b[0m \u001b[90mboasts\u001b[0m \u001b[90ma\u001b[0m\n",
      "\u001b[90mkiddie\u001b[0m \u001b[90mzip\u001b[0m \u001b[90mline.\u001b[0m \u001b[90mThere\u001b[0m \u001b[90mare\u001b[0m \u001b[90malso\u001b[0m \u001b[90mpicnic\u001b[0m \u001b[90mtables\u001b[0m \u001b[90mand\u001b[0m \u001b[90ma\u001b[0m \u001b[90mlarge\u001b[0m \u001b[90mgrassy\u001b[0m \u001b[90marea\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mwashrooms.\u001b[0m \u001b[90mIt’s\u001b[0m \u001b[90ma\u001b[0m\n",
      "\u001b[31mgreat\u001b[0m \u001b[90mfamily\u001b[0m \u001b[31mpark\u001b[0m \u001b[90mat\u001b[0m \u001b[90mthe\u001b[0m \u001b[31mbeach\u001b[0m \u001b[90mwith\u001b[0m \u001b[90mfree\u001b[0m \u001b[90mparking\u001b[0m \u001b[90mlot\u001b[0m\n",
      "\n",
      "\n",
      "Royal BC Museum\n",
      "------------------\n",
      "\u001b[90mGreat\u001b[0m \u001b[90mmuseum\u001b[0m \u001b[90mhas\u001b[0m \u001b[90mvery\u001b[0m \u001b[90mnice\u001b[0m \u001b[90mand\u001b[0m \u001b[90mextensive\u001b[0m \u001b[90mexhibits.\u001b[0m \u001b[31mI\u001b[0m \u001b[90mwent\u001b[0m \u001b[90mthere\u001b[0m \u001b[90mwhen\u001b[0m \u001b[90mthey\u001b[0m \u001b[90mhad\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mwildlife\u001b[0m \u001b[90mphoto-\n",
      "journalism\u001b[0m \u001b[90mexhibit\u001b[0m \u001b[90mwhich\u001b[0m \u001b[90mwas\u001b[0m \u001b[90ma\u001b[0m \u001b[90minteresting\u001b[0m \u001b[90mone.\u001b[0m \u001b[90mIt\u001b[0m \u001b[90mwasn’t\u001b[0m \u001b[90mbusy\u001b[0m \u001b[90mwhatsoever\u001b[0m \u001b[90mbut\u001b[0m \u001b[31mI\u001b[0m \u001b[90mdid\u001b[0m \u001b[90mgo\u001b[0m \u001b[90mon\u001b[0m \u001b[90ma\u001b[0m\n",
      "\u001b[90mrainy\u001b[0m \u001b[90mSunday\u001b[0m \u001b[90maround\u001b[0m \u001b[90m3:00.\u001b[0m \u001b[90mWould\u001b[0m \u001b[90mdefinitely\u001b[0m \u001b[90mrecommend\u001b[0m \u001b[90mgoing.\u001b[0m \u001b[90mIt\u001b[0m \u001b[90malso\u001b[0m \u001b[90mseems\u001b[0m \u001b[90mlike\u001b[0m \u001b[90mthere\u001b[0m \u001b[90mwas\u001b[0m \u001b[90ma\u001b[0m \u001b[90mthird\u001b[0m\n",
      "\u001b[90mfloor\u001b[0m \u001b[90mexhibit\u001b[0m \u001b[90mbut\u001b[0m \u001b[90mfor\u001b[0m \u001b[90msome\u001b[0m \u001b[90mreason\u001b[0m \u001b[90mit’s\u001b[0m \u001b[90mclosed\u001b[0m \u001b[90mand\u001b[0m \u001b[31mI\u001b[0m \u001b[90mdon’t\u001b[0m \u001b[90mknow\u001b[0m \u001b[90mwhy.\u001b[0m \u001b[90mMaybe\u001b[0m \u001b[90mit\u001b[0m \u001b[90mwill\u001b[0m \u001b[90mbe\u001b[0m\n",
      "\u001b[90mopen\u001b[0m \u001b[90msometime\u001b[0m \u001b[90min\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mfuture.\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mGrand\u001b[0m \u001b[90mDame\u001b[0m \u001b[90mof\u001b[0m \u001b[90mMuseums\u001b[0m \u001b[90min\u001b[0m \u001b[90mVictoria,\u001b[0m \u001b[90mBC.\u001b[0m \u001b[90mIn\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mheart\u001b[0m \u001b[90mof\u001b[0m\n",
      "\u001b[90mthe\u001b[0m \u001b[90mcity;\u001b[0m \u001b[90mnext\u001b[0m \u001b[90mto\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mIconic\u001b[0m \u001b[90mEmpress\u001b[0m \u001b[90mHotel\u001b[0m \u001b[90mand\u001b[0m \u001b[90macross\u001b[0m \u001b[90mfrom\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mFamed\u001b[0m \u001b[90mVictoria\u001b[0m \u001b[90mHarbour.\u001b[0m \u001b[90mThe\u001b[0m\n",
      "\u001b[90mdisplays\u001b[0m \u001b[90min\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mdifferent\u001b[0m \u001b[90mthemed\u001b[0m \u001b[90mexhibits\u001b[0m \u001b[90mare\u001b[0m \u001b[90mawesome!\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mextensive\u001b[0m \u001b[90minformation\u001b[0m \u001b[90mand\u001b[0m \u001b[90mdetail\u001b[0m \u001b[90min\u001b[0m \u001b[90mthe\u001b[0m\n",
      "\u001b[90mdisplays\u001b[0m \u001b[90mis\u001b[0m \u001b[90mfabulous.\u001b[0m \u001b[90mPlan\u001b[0m \u001b[90mon\u001b[0m \u001b[90mspending\u001b[0m \u001b[90ma\u001b[0m \u001b[90mminimum\u001b[0m \u001b[90mof\u001b[0m \u001b[90mthree\u001b[0m \u001b[90mhours;\u001b[0m \u001b[90mmore,\u001b[0m \u001b[90mif\u001b[0m \u001b[90myour\u001b[0m \u001b[90ma\u001b[0m \u001b[31mhistory\u001b[0m \u001b[90mbuff!\u001b[0m\n",
      "\u001b[90mThis\u001b[0m \u001b[90mis\u001b[0m \u001b[90mone\u001b[0m \u001b[90mof\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mbest\u001b[0m \u001b[31mmuseums\u001b[0m \u001b[90mwe\u001b[0m \u001b[90mhave\u001b[0m \u001b[90mvisited.\u001b[0m \u001b[90mIt\u001b[0m \u001b[90mhas\u001b[0m \u001b[90mnatural\u001b[0m \u001b[90mhistory,\u001b[0m \u001b[90mart,\u001b[0m \u001b[90mFirst\u001b[0m \u001b[90mPeople\u001b[0m\n",
      "\u001b[90mcultural\u001b[0m \u001b[90mhistory,\u001b[0m \u001b[90mand\u001b[0m \u001b[90man\u001b[0m \u001b[90minteractive\u001b[0m \u001b[90mtown/ship/\u001b[0m \u001b[90mmine\u001b[0m \u001b[90mfrom\u001b[0m \u001b[90mBritish\u001b[0m \u001b[90mcolonial\u001b[0m \u001b[90mhistory.\u001b[0m \u001b[90mReally\u001b[0m \u001b[90mgreat.\u001b[0m \u001b[90mTwo\u001b[0m \u001b[90mweeks\u001b[0m\n",
      "\u001b[90mago,\u001b[0m \u001b[90mthat\u001b[0m \u001b[90msection\u001b[0m \u001b[90mwas\u001b[0m \u001b[90mclosed\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mremodel.\u001b[0m \u001b[31mI'm\u001b[0m \u001b[90mhoping\u001b[0m \u001b[90mit\u001b[0m \u001b[90mis\u001b[0m \u001b[90mstill\u001b[0m \u001b[90mas\u001b[0m \u001b[90mawesome\u001b[0m \u001b[90mas\u001b[0m \u001b[90mever\u001b[0m \u001b[90mwhen\u001b[0m\n",
      "\u001b[90mit\u001b[0m \u001b[90mreopens.\u001b[0m \u001b[90mGreat\u001b[0m \u001b[31mplace\u001b[0m \u001b[90mto\u001b[0m \u001b[90mvisit\u001b[0m \u001b[90mand\u001b[0m \u001b[31mlearn\u001b[0m \u001b[90mabout\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mice\u001b[0m \u001b[90mage\u001b[0m \u001b[90mand\u001b[0m \u001b[90mstuff.\u001b[0m \u001b[90mThey\u001b[0m \u001b[90meven\u001b[0m \u001b[90mhad\u001b[0m \u001b[90ma\u001b[0m\n",
      "\u001b[90mshowing\u001b[0m \u001b[90mof\u001b[0m \u001b[90mcovid\u001b[0m \u001b[90mportraits\u001b[0m \u001b[90mwhich\u001b[0m \u001b[90mwas\u001b[0m \u001b[90mcool.\u001b[0m \u001b[90mLikes:\u001b[0m \u001b[90mit\u001b[0m \u001b[90mwas\u001b[0m \u001b[90mcheap.\u001b[0m \u001b[90m$5\u001b[0m \u001b[90mper\u001b[0m \u001b[90mperson\u001b[0m \u001b[90mand\u001b[0m \u001b[90mit\u001b[0m \u001b[90mwas\u001b[0m\n",
      "\u001b[90mabsolutely\u001b[0m \u001b[90mworth\u001b[0m \u001b[90mit.\u001b[0m \u001b[90mLots\u001b[0m \u001b[90mto\u001b[0m \u001b[90msee\u001b[0m \u001b[90mand\u001b[0m \u001b[90mlearn.\u001b[0m \u001b[90mDislikes:\u001b[0m \u001b[90mNothing\u001b[0m \u001b[31mI\u001b[0m \u001b[90mcan\u001b[0m \u001b[90mthink\u001b[0m \u001b[90mof\u001b[0m \u001b[90mMust\u001b[0m \u001b[90mvisit!!\u001b[0m \u001b[90mBut\u001b[0m\n",
      "\u001b[90mset\u001b[0m \u001b[90maside\u001b[0m \u001b[90mat\u001b[0m \u001b[90mleast\u001b[0m \u001b[90m3\u001b[0m \u001b[90mhours\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mthis\u001b[0m \u001b[90mplace.\u001b[0m \u001b[90mOn\u001b[0m \u001b[90mSaturdays(Summers\u001b[0m \u001b[31mI\u001b[0m \u001b[90mthink)\u001b[0m \u001b[90mthey\u001b[0m \u001b[90mare\u001b[0m \u001b[90mopen\u001b[0m \u001b[90muntil\u001b[0m\n",
      "\u001b[90m10pm,\u001b[0m \u001b[90mwhich\u001b[0m \u001b[90mworked\u001b[0m \u001b[90min\u001b[0m \u001b[90mour\u001b[0m \u001b[90mfavour.There\u001b[0m \u001b[90mis\u001b[0m \u001b[90mso\u001b[0m \u001b[90mmuch\u001b[0m \u001b[90minformation\u001b[0m \u001b[90mto\u001b[0m \u001b[90mabsorb\u001b[0m \u001b[90min\u001b[0m \u001b[90mlimited\u001b[0m \u001b[90mtime.\u001b[0m \u001b[90mDuring\u001b[0m\n",
      "\u001b[90mour\u001b[0m \u001b[90mvisit\u001b[0m \u001b[90m-\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mfirst\u001b[0m \u001b[90mfloor\u001b[0m \u001b[90mwas\u001b[0m \u001b[90mdedicated\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mFirst\u001b[0m \u001b[90mNations,\u001b[0m \u001b[90mSecond\u001b[0m \u001b[90mfloor\u001b[0m \u001b[90mof\u001b[0m \u001b[90mAnimal\u001b[0m \u001b[90mevolution,\u001b[0m\n",
      "\u001b[90mThird\u001b[0m \u001b[90mfloor\u001b[0m \u001b[90mwas\u001b[0m \u001b[90mset\u001b[0m \u001b[90maside\u001b[0m \u001b[90mfor\u001b[0m \u001b[90mStu\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mdinosaur\u001b[0m \u001b[90m(T-Rex\u001b[0m \u001b[90mfossil\u001b[0m \u001b[90mdiscovered\u001b[0m \u001b[90min\u001b[0m \u001b[90mCanada)\u001b[0m \u001b[90mand\u001b[0m \u001b[90mthe\u001b[0m\n",
      "\u001b[90mrelated\u001b[0m \u001b[90mdiscoveries.\u001b[0m \u001b[90mWe\u001b[0m \u001b[90malso\u001b[0m \u001b[90mhad\u001b[0m \u001b[90mthe\u001b[0m \u001b[90mopportunity\u001b[0m \u001b[90mto\u001b[0m \u001b[90mvisit\u001b[0m \u001b[90m-The\u001b[0m \u001b[90mAngkor\u001b[0m \u001b[90mwat\u001b[0m \u001b[90mdisplay-\u001b[0m \u001b[90mwhich\u001b[0m \u001b[90mwas\u001b[0m \u001b[90mexcellent,\u001b[0m\n",
      "\u001b[90mbut\u001b[0m \u001b[90mmany\u001b[0m \u001b[90mof\u001b[0m \u001b[90mthe\u001b[0m \u001b[90midols\u001b[0m \u001b[90mwere\u001b[0m \u001b[90mmissing\u001b[0m \u001b[90mparts\u001b[0m \u001b[90mwhile\u001b[0m \u001b[90mon\u001b[0m \u001b[90mexhibition\u001b[0m \u001b[90m:(.\u001b[0m \u001b[90mThe\u001b[0m \u001b[90mmuseum\u001b[0m \u001b[90mis\u001b[0m \u001b[90mwell-maintained,\u001b[0m\n",
      "\u001b[90mclean,\u001b[0m \u001b[90mwheelchair\u001b[0m \u001b[90maccessible\u001b[0m \u001b[90mand\u001b[0m \u001b[90mhas\u001b[0m \u001b[90mvery\u001b[0m \u001b[90mfriendly\u001b[0m \u001b[90mstaff.\u001b[0m\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from textwrap import wrap\n",
    "reset = \"\\x1b[0m\"\n",
    "blue = \"\\x1b[34m\"\n",
    "red = \"\\x1b[31m\"\n",
    "gray = \"\\x1b[90m\"\n",
    "# print the reviews in the recommended DataFrame but in a way that the text fits in the notebook cell output\n",
    "for index, row in recommended.iterrows():\n",
    "    print(row['name'])\n",
    "    print(\"------------------\")\n",
    "\n",
    "    words = row['reviews'].split()\n",
    "    highlighted = [red + word + reset if word in user_input_no_stop.split() else gray + word + reset for word in words]\n",
    "\n",
    "    print('\\n'.join(wrap(\" \".join(highlighted), 250)))\n",
    "    print('\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this is one approach for making recomendations it has some serious pitfalls.\n",
    "\n",
    "Once of which is that the model does not understand negative sentiment in the user input. For example, if a user says that they dont' like a certain activity the model will not take that into account.\n",
    "\n",
    "Anothe issue is that the model is using naive tokens. i.e single words and is unable to distinguish between someone saying \"great sunset\" or \"great food\"."
   ]
  }
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