yelp_regression.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Project: Yelp Rating Regression Predictor\n",
    "\n",
    "The restaurant industry is tougher than ever, with restaurant reviews blazing across the Internet from day one of a restaurant's opening. But as a lover of food, you and your friend decide to break into the industry and open up your own restaurant, Danielle's Delicious Delicacies. Since a restaurant's success is highly correlated with its reputation, you want to make sure Danielle's Delicious Delicacies has the best reviews on the most queried restaurant review site: Yelp! While you know your food will be delicious, you think there are other factors that play into a Yelp rating and will ultimately determine your business's success. With a dataset of different restaurant features and their Yelp ratings, you decide to use a Multiple Linear Regression model to investigate what factors most affect a restaurant's Yelp rating and predict the Yelp rating for your restaurant!\n",
    "\n",
    "In this project we'll be working with a real dataset provided by Yelp. We have provided six files, listed below with a brief description:\n",
    "* `yelp_business.json`: establishment data regarding location and attributes for all businesses in the dataset\n",
    "* `yelp_review.json`: Yelp review metadata by business\n",
    "* `yelp_user.json`: user profile metadata by business\n",
    "* `yelp_checkin.json`: online checkin metadata by business\n",
    "* `yelp_tip.json`: tip metadata by business\n",
    "* `yelp_photo.json`: photo metadata by business\n",
    "\n",
    "For a more detailed explanation of the features in each `.json` file, see the accompanying [explanatory feature document](https://docs.google.com/document/d/1V6FjJpKspVBOOBs4E7fBfp_yzHn0--XJkC2uUtWuRgM/edit).\n",
    "\n",
    "Let's get started by exploring the data in each of these files to see what we are working with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data and Take a Peek\n",
    "\n",
    "To get a better understanding of the dataset we can use Pandas to explore the data in DataFrame form. In the code block below we have imported Pandas for you. The `read_json()` method reads data from a json file into a DataFrame, as shown below:\n",
    "```python\n",
    "df = pd.read_json('file_name.json', lines=True)\n",
    "```\n",
    "Load the data from each of the json files with the following naming conventions:\n",
    "* `yelp_business.json` into a DataFrame named `businesses`\n",
    "* `yelp_review.json` into a DataFrame named `reviews`\n",
    "* `yelp_user.json` into a DataFrame named `users`\n",
    "* `yelp_checkin.json` into a DataFrame named `checkins`\n",
    "* `yelp_tip.json` into a DataFrame named `tips`\n",
    "* `yelp_photo.json` into a DataFrame named `photos`\n",
    "\n",
    "Importing that data could take 10 to 20 seconds to run depending on your computer, but don't worry, once it's loaded in you're ready to go!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "businesses = pd.read_json('yelp_business.json',lines=True)\n",
    "reviews = pd.read_json('yelp_review.json',lines=True)\n",
    "users = pd.read_json('yelp_user.json',lines=True)\n",
    "checkins = pd.read_json('yelp_checkin.json',lines=True)\n",
    "tips = pd.read_json('yelp_tip.json',lines=True)\n",
    "photos = pd.read_json('yelp_photo.json',lines=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to more clearly see the information in our DataFrame, we can adjust the number of columns shown (`max_columns`) and the number of characters shown in a column (`max_colwidth`) with the below code:\n",
    "\n",
    "```python\n",
    "pd.options.display.max_columns = number_of_columns_to_display\n",
    "pd.options.display.max_colwidth = number_of_characters_to_display\n",
    "```\n",
    "\n",
    "Set `max_columns` to `60` and `max_colwidth` to `500`. We are working with some BIG data here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:21.351977Z",
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   "source": [
    "pd.options.display.max_columns = 60\n",
    "pd.options.display.max_colwidth = 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the first five rows of each DataFrame using the `.head()` method to get an overview of the data (make sure to check each DataFrame in a separate cell in order to view it properly)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    "ExecuteTime": {
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     "start_time": "2018-09-14T14:51:21.354525Z"
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       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>alcohol?</th>\n",
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       "      <th>...</th>\n",
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       "      <td>1314 44 Avenue NE</td>\n",
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       "      <td>{'Monday': '8:30-17:0', 'Tuesday': '11:0-21:0'...</td>\n",
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       "      <td>Minhas Micro Brewery</td>\n",
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       "      <td>{'Friday': '17:0-23:0', 'Saturday': '17:0-23:0...</td>\n",
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       "      <td>CK'S BBQ &amp; Catering</td>\n",
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       "      <td>1</td>\n",
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       "      <td>O8S5hYJ1SMc8fA4QBtVujA</td>\n",
       "      <td>Breakfast &amp; Brunch, Restaurants, French, Sandw...</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Monday': '10:0-22:0', 'Tuesday': '10:0-22:0'...</td>\n",
       "      <td>...</td>\n",
       "      <td>-73.599300</td>\n",
       "      <td>La Bastringue</td>\n",
       "      <td>Rosemont-La Petite-Patrie</td>\n",
       "      <td>H2G 1K7</td>\n",
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       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>211 W Monroe St</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>bFzdJJ3wp3PZssNEsyU23g</td>\n",
       "      <td>Insurance, Financial Services</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>-112.076979</td>\n",
       "      <td>Geico Insurance</td>\n",
       "      <td></td>\n",
       "      <td>85003</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>AZ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005 Alyth Place SE</td>\n",
       "      <td>0</td>\n",
       "      <td>{'BusinessAcceptsCreditCards': 'True'}</td>\n",
       "      <td>8USyCYqpScwiNEb58Bt6CA</td>\n",
       "      <td>Home &amp; Garden, Nurseries &amp; Gardening, Shopping...</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>{'Monday': '8:0-17:0', 'Tuesday': '8:0-17:0', ...</td>\n",
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       "      <td>Action Engine</td>\n",
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       "               address  alcohol?  \\\n",
       "0    1314 44 Avenue NE         0   \n",
       "1                              0   \n",
       "2  1335 rue Beaubien E         1   \n",
       "3      211 W Monroe St         0   \n",
       "4  2005 Alyth Place SE         0   \n",
       "\n",
       "                                          attributes             business_id  \\\n",
       "0  {'BikeParking': 'False', 'BusinessAcceptsCredi...  Apn5Q_b6Nz61Tq4XzPdf9A   \n",
       "1  {'Alcohol': 'none', 'BikeParking': 'False', 'B...  AjEbIBw6ZFfln7ePHha9PA   \n",
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       "3                                               None  bFzdJJ3wp3PZssNEsyU23g   \n",
       "4             {'BusinessAcceptsCreditCards': 'True'}  8USyCYqpScwiNEb58Bt6CA   \n",
       "\n",
       "                                          categories       city  \\\n",
       "0  Tours, Breweries, Pizza, Restaurants, Food, Ho...    Calgary   \n",
       "1  Chicken Wings, Burgers, Caterers, Street Vendo...  Henderson   \n",
       "2  Breakfast & Brunch, Restaurants, French, Sandw...   Montréal   \n",
       "3                      Insurance, Financial Services    Phoenix   \n",
       "4  Home & Garden, Nurseries & Gardening, Shopping...    Calgary   \n",
       "\n",
       "   good_for_kids  has_bike_parking  has_wifi  \\\n",
       "0              1                 0         0   \n",
       "1              1                 0         0   \n",
       "2              1                 1         1   \n",
       "3              0                 0         0   \n",
       "4              0                 0         0   \n",
       "\n",
       "                                               hours  ...   longitude  \\\n",
       "0  {'Monday': '8:30-17:0', 'Tuesday': '11:0-21:0'...  ... -114.031675   \n",
       "1  {'Friday': '17:0-23:0', 'Saturday': '17:0-23:0...  ... -114.939821   \n",
       "2  {'Monday': '10:0-22:0', 'Tuesday': '10:0-22:0'...  ...  -73.599300   \n",
       "3                                               None  ... -112.076979   \n",
       "4  {'Monday': '8:0-17:0', 'Tuesday': '8:0-17:0', ...  ... -114.027366   \n",
       "\n",
       "                   name               neighborhood postal_code price_range  \\\n",
       "0  Minhas Micro Brewery                                T2E 6L6           2   \n",
       "1   CK'S BBQ & Catering                                  89002           2   \n",
       "2         La Bastringue  Rosemont-La Petite-Patrie     H2G 1K7           2   \n",
       "3       Geico Insurance                                  85003           0   \n",
       "4         Action Engine                                T2H 0N5           0   \n",
       "\n",
       "  review_count  stars  state  take_reservations takes_credit_cards  \n",
       "0           24    4.0     AB                  1                  1  \n",
       "1            3    4.5     NV                  0                  1  \n",
       "2            5    4.0     QC                  1                  0  \n",
       "3            8    1.5     AZ                  0                  0  \n",
       "4            4    2.0     AB                  0                  1  \n",
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      "text/plain": [
       "              business_id  average_review_age  average_review_length  \\\n",
       "0  --1UhMGODdWsrMastO9DZw          524.458333             466.208333   \n",
       "1  --6MefnULPED_I942VcFNA         1199.589744             785.205128   \n",
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       "4  --9QQLMTbFzLJ_oT-ON3Xw          978.727273             436.181818   \n",
       "\n",
       "   average_review_sentiment  number_funny_votes  number_cool_votes  \\\n",
       "0                  0.808638                   1                 16   \n",
       "1                  0.669126                  27                 32   \n",
       "2                  0.820837                  29                 52   \n",
       "3                  0.170925                   0                  0   \n",
       "4                  0.562264                   3                  4   \n",
       "\n",
       "   number_useful_votes  \n",
       "0                   15  \n",
       "1                   53  \n",
       "2                   81  \n",
       "3                    9  \n",
       "4                    7  "
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     "execution_count": 3,
     "metadata": {},
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>--9QQLMTbFzLJ_oT-ON3Xw</td>\n",
       "      <td>52.454545</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>34.636364</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id  average_number_friends  average_days_on_yelp  \\\n",
       "0  --1UhMGODdWsrMastO9DZw               18.791667           1789.750000   \n",
       "1  --6MefnULPED_I942VcFNA              214.564103           2039.948718   \n",
       "2  --7zmmkVg-IMGaXbuVd0SQ              126.185185           1992.796296   \n",
       "3  --8LPVSo5i0Oo61X01sV9A               25.250000           2095.750000   \n",
       "4  --9QQLMTbFzLJ_oT-ON3Xw               52.454545           1804.636364   \n",
       "\n",
       "   average_number_fans  average_review_count  average_number_years_elite  \n",
       "0             1.833333             57.541667                    0.833333  \n",
       "1            49.256410            332.743590                    1.769231  \n",
       "2            19.222222            208.962963                    1.814815  \n",
       "3             0.500000              7.500000                    0.000000  \n",
       "4             1.000000             34.636364                    0.090909  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:21.433534Z",
     "start_time": "2018-09-14T14:51:21.421393Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "              business_id                                               time  \\\n",
       "0  7KPBkxAOEtb3QeIL9PEErg  {'Fri-0': 2, 'Sat-0': 1, 'Sun-0': 1, 'Wed-0': ...   \n",
       "1  kREVIrSBbtqBhIYkTccQUg  {'Mon-13': 1, 'Thu-13': 1, 'Sat-16': 1, 'Wed-1...   \n",
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       "\n",
       "   weekday_checkins  weekend_checkins  \n",
       "0                76                75  \n",
       "1                 4                 3  \n",
       "2                 3                 3  \n",
       "3                 1                 2  \n",
       "4                 1                 4  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "checkins.head()"
   ]
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  {
   "cell_type": "code",
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    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:21.445061Z",
     "start_time": "2018-09-14T14:51:21.435743Z"
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    "scrolled": true
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       "              business_id  average_tip_length  number_tips\n",
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   "source": [
    "tips.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:21.455113Z",
     "start_time": "2018-09-14T14:51:21.446975Z"
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       "              business_id  average_caption_length  number_pics\n",
       "0  --1UhMGODdWsrMastO9DZw                0.000000            1\n",
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many different businesses are in the dataset? What are the different features in the review DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188593"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(businesses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:21.460856Z",
     "start_time": "2018-09-14T14:51:21.456994Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['business_id', 'average_number_friends', 'average_days_on_yelp',\n",
       "       'average_number_fans', 'average_review_count',\n",
       "       'average_number_years_elite'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the range of values for the features in the user DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:21.556908Z",
     "start_time": "2018-09-14T14:51:21.462876Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_number_friends</th>\n",
       "      <th>average_days_on_yelp</th>\n",
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       "      <th>count</th>\n",
       "      <td>188593.000000</td>\n",
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       "      <td>188593.000000</td>\n",
       "      <td>188593.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>105.132000</td>\n",
       "      <td>2005.367009</td>\n",
       "      <td>11.590148</td>\n",
       "      <td>122.110660</td>\n",
       "      <td>0.923313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>162.653680</td>\n",
       "      <td>554.174540</td>\n",
       "      <td>25.901801</td>\n",
       "      <td>208.814471</td>\n",
       "      <td>1.109289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.666667</td>\n",
       "      <td>1647.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59.384615</td>\n",
       "      <td>1957.150000</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>62.142857</td>\n",
       "      <td>0.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.666667</td>\n",
       "      <td>2312.238095</td>\n",
       "      <td>11.555556</td>\n",
       "      <td>143.454545</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4219.000000</td>\n",
       "      <td>4860.000000</td>\n",
       "      <td>1174.666667</td>\n",
       "      <td>6335.000000</td>\n",
       "      <td>10.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_number_friends  average_days_on_yelp  average_number_fans  \\\n",
       "count           188593.000000         188593.000000        188593.000000   \n",
       "mean               105.132000           2005.367009            11.590148   \n",
       "std                162.653680            554.174540            25.901801   \n",
       "min                  1.000000             76.000000             0.000000   \n",
       "25%                 26.666667           1647.000000             0.666667   \n",
       "50%                 59.384615           1957.150000             3.583333   \n",
       "75%                117.666667           2312.238095            11.555556   \n",
       "max               4219.000000           4860.000000          1174.666667   \n",
       "\n",
       "       average_review_count  average_number_years_elite  \n",
       "count         188593.000000               188593.000000  \n",
       "mean             122.110660                    0.923313  \n",
       "std              208.814471                    1.109289  \n",
       "min                0.666667                    0.000000  \n",
       "25%               21.000000                    0.000000  \n",
       "50%               62.142857                    0.583333  \n",
       "75%              143.454545                    1.400000  \n",
       "max             6335.000000                   10.666667  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the Yelp rating, or `stars`, of the establishment with `business_id` = `5EvUIR4IzCWUOm0PsUZXjA`. Use Pandas boolean indexing to find the Yelp rating, using the syntax below:\n",
    "\n",
    "```python\n",
    "df[df['column_we_know'] == 'value_we_know']['column_we_want']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:21.598473Z",
     "start_time": "2018-09-14T14:51:21.559796Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30781    3.0\n",
       "Name: stars, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "businesses[businesses['business_id'] == '5EvUIR4IzCWUOm0PsUZXjA']['stars']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " What feature, or column, do the DataFrames have in common?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/huginn/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Index.__and__ operating as a set operation is deprecated, in the future this will be a logical operation matching Series.__and__.  Use index.intersection(other) instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['business_id'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "businesses.columns & reviews.columns & users.columns & checkins.columns & tips.columns & photos.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge the Data\n",
    "\n",
    "Since we are working with data from several files, we need to combine the data into a single DataFrame that allows us to analyze the different features with respect to our target variable, the Yelp rating. We can do this by merging the multiple DataFrames we have together, joining them on the columns they have in common. In our case, this unique identifying column is the `business_id`. We can merge two DataFrames together with the following syntax:\n",
    "\n",
    "```python\n",
    "pd.merge(left, right, how='inner/outer/left/right', on='column(s)_to_merge_on')\n",
    "```\n",
    "* `left` is the DataFrame on the left side of our merge\n",
    "* `right` is the DataFrame on the right side of our merge\n",
    "* `how` describes the style of merge we want to complete (similar to inner/outer/left/right joins in SQL)\n",
    "* `on` is the column or columns to perform the merge on (the column connecting the two tables)\n",
    "\n",
    "Given our six DataFrames, we will need to perform 5 merges to combine all the data into one DataFrame. In the cell below we merged the business table and the review table into a new DataFrame, `df`, for you. After the merge we've added all the rows from `businesses` and `reviews` together, but kept the same total number of rows! Run the cell to perform the merge and confirm the number of rows in `df`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188593"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(businesses, reviews, how='left', on='business_id')\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge each of the other 4 DataFrames into our new DataFrame `df` to combine all the data together. Make sure that `df` is the left DataFrame in each merge and `how=left` since not every DataFrame includes every business in the dataset (this way we won't lose any data during the merges). Once combined, print out the columns of `df`. What features are in this new DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:24.043166Z",
     "start_time": "2018-09-14T14:51:22.538823Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.merge(df, users, how='left', on='business_id')\n",
    "df = pd.merge(df, checkins, how='left', on='business_id')\n",
    "df = pd.merge(df, tips, how='left', on='business_id')\n",
    "df = pd.merge(df, photos, how='left', on='business_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['address', 'alcohol?', 'attributes', 'business_id', 'categories',\n",
       "       'city', 'good_for_kids', 'has_bike_parking', 'has_wifi', 'hours',\n",
       "       'is_open', 'latitude', 'longitude', 'name', 'neighborhood',\n",
       "       'postal_code', 'price_range', 'review_count', 'stars', 'state',\n",
       "       'take_reservations', 'takes_credit_cards', 'average_review_age',\n",
       "       'average_review_length', 'average_review_sentiment',\n",
       "       'number_funny_votes', 'number_cool_votes', 'number_useful_votes',\n",
       "       'average_number_friends', 'average_days_on_yelp', 'average_number_fans',\n",
       "       'average_review_count', 'average_number_years_elite', 'time',\n",
       "       'weekday_checkins', 'weekend_checkins', 'average_tip_length',\n",
       "       'number_tips', 'average_caption_length', 'number_pics'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean the Data\n",
    "\n",
    "We are getting really close to the fun analysis part! We just have to clean our data a bit so we can focus on the features that might have predictive power for determining an establishment's Yelp rating.\n",
    "\n",
    "In a Linear Regression model, our features will ideally be continuous variables that have an affect on our dependent variable, the Yelp rating. For this project with will also be working with some features that are binary, on the scale [0,1]. With this information, we can remove any columns in the dataset that are not continuous or binary, and that we do not want to make predictions on. The cell below contains a list of these unnecessary features. Drop them from `df` with Pandas' drop syntax, provided below:\n",
    "\n",
    "```python\n",
    "df.drop(list_of_features_to_remove, axis=1, inplace=True)\n",
    "```\n",
    "\n",
    "* `list_of_features_to_remove` is, you guessed it, the list of features we want to remove!\n",
    "* `axis=1` lets Pandas know we want to drop columns, not rows, from our DataFrame (axis=0 is used for computations along rows!) \n",
    "* `inplace=True` lets us drop the columns right here in our DataFrame, instead of returning a new DataFrame that we could store in a new variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_to_remove = ['address','attributes','business_id','categories','city','hours','is_open','latitude','longitude','name','neighborhood','postal_code','state','time']\n",
    "df.drop(labels=features_to_remove, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just have to check our data to make sure we don't have any missing values, or `NaN`s, which will prevent the Linear Regression model from running correctly. To do this we can use the statement `df.isna().any()`. This will check all of our columns and return `True` if there are any missing values or `NaN`s, or `False` if there are no missing values. Check if `df` is missing any values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:24.649109Z",
     "start_time": "2018-09-14T14:51:24.581948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol?                      False\n",
       "good_for_kids                 False\n",
       "has_bike_parking              False\n",
       "has_wifi                      False\n",
       "price_range                   False\n",
       "review_count                  False\n",
       "stars                         False\n",
       "take_reservations             False\n",
       "takes_credit_cards            False\n",
       "average_review_age            False\n",
       "average_review_length         False\n",
       "average_review_sentiment      False\n",
       "number_funny_votes            False\n",
       "number_cool_votes             False\n",
       "number_useful_votes           False\n",
       "average_number_friends        False\n",
       "average_days_on_yelp          False\n",
       "average_number_fans           False\n",
       "average_review_count          False\n",
       "average_number_years_elite    False\n",
       "weekday_checkins               True\n",
       "weekend_checkins               True\n",
       "average_tip_length             True\n",
       "number_tips                    True\n",
       "average_caption_length         True\n",
       "number_pics                    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there are a few columns with missing values. Since our dataset has no information recorded for some businesses in these columns, we will assume the Yelp pages did not display these features. For example, if there is a `NaN` value for `number_pics`, it means that the associated business did not have any pictures posted on its Yelp page. Thus we can replace all of our `NaN`s with `0`s. To do this we can use the `.fillna()` method, which takes a dictionary as shown below:\n",
    "\n",
    "```python\n",
    "df.fillna({'column_1':val_to_replace_na,\n",
    "           'column_2':val_to_replace_na,\n",
    "           'column_3':val_to_replace_na},\n",
    "          inplace=True)\n",
    "```\n",
    "\n",
    "* `column_1`, `column_2`, and `column_3` are the columns with missing values that we want to fill. We can include as many columns as we like in the dictionary that is passed to `.fill_na()`\n",
    "* `val_to_replace_na` is the value that will replace the missing values, or `NaN`s\n",
    "* `inplace=True` since we want to perform our changes in place and not return a new DataFrame\n",
    "\n",
    "Fill the missing values in `df` with `0`. Afterwards, confirm the missing values have been filled with `df.isna().any()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:24.661609Z",
     "start_time": "2018-09-14T14:51:24.650866Z"
    }
   },
   "outputs": [],
   "source": [
    "df.fillna({'weekday_checkins':0,\n",
    "           'weekend_checkins':0,\n",
    "           'average_tip_length':0,\n",
    "           'number_tips':0,\n",
    "           'average_caption_length':0,\n",
    "           'number_pics':0},\n",
    "          inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol?                      False\n",
       "good_for_kids                 False\n",
       "has_bike_parking              False\n",
       "has_wifi                      False\n",
       "price_range                   False\n",
       "review_count                  False\n",
       "stars                         False\n",
       "take_reservations             False\n",
       "takes_credit_cards            False\n",
       "average_review_age            False\n",
       "average_review_length         False\n",
       "average_review_sentiment      False\n",
       "number_funny_votes            False\n",
       "number_cool_votes             False\n",
       "number_useful_votes           False\n",
       "average_number_friends        False\n",
       "average_days_on_yelp          False\n",
       "average_number_fans           False\n",
       "average_review_count          False\n",
       "average_number_years_elite    False\n",
       "weekday_checkins              False\n",
       "weekend_checkins              False\n",
       "average_tip_length            False\n",
       "number_tips                   False\n",
       "average_caption_length        False\n",
       "number_pics                   False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis\n",
    "\n",
    "Now that our data is all together, let's investigate some of the different features to see what might correlate most with our dependent variable, the Yelp rating (called `stars` in our DataFrame). The features with the best correlations could prove to be the most helpful for our Linear Regression model! Pandas DataFrames have a really helpful method, `.corr()`, that allows us to see the correlation coefficients for each pair of our different features. Remember, a correlation of `0` indicates that two features have no linear relationship, a correlation coefficient of `1` indicates two features have a perfect positive linear relationship, and a correlation coefficient of `-1` indicates two features have a perfect negative linear relationship. Call `.corr()` on `df`. You'll see that `number_funny_votes` has a correlation coefficient of `0.001320` with respect to `stars`, our Yelp rating. This is a very weak correlation. What features best correlate, both positively and negatively, with Yelp rating?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:24.968871Z",
     "start_time": "2018-09-14T14:51:24.725501Z"
    },
    "scrolled": true
   },
   "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>alcohol?</th>\n",
       "      <th>good_for_kids</th>\n",
       "      <th>has_bike_parking</th>\n",
       "      <th>has_wifi</th>\n",
       "      <th>price_range</th>\n",
       "      <th>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>take_reservations</th>\n",
       "      <th>takes_credit_cards</th>\n",
       "      <th>average_review_age</th>\n",
       "      <th>average_review_length</th>\n",
       "      <th>average_review_sentiment</th>\n",
       "      <th>number_funny_votes</th>\n",
       "      <th>number_cool_votes</th>\n",
       "      <th>number_useful_votes</th>\n",
       "      <th>average_number_friends</th>\n",
       "      <th>average_days_on_yelp</th>\n",
       "      <th>average_number_fans</th>\n",
       "      <th>average_review_count</th>\n",
       "      <th>average_number_years_elite</th>\n",
       "      <th>weekday_checkins</th>\n",
       "      <th>weekend_checkins</th>\n",
       "      <th>average_tip_length</th>\n",
       "      <th>number_tips</th>\n",
       "      <th>average_caption_length</th>\n",
       "      <th>number_pics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alcohol?</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.305284</td>\n",
       "      <td>0.213318</td>\n",
       "      <td>0.345032</td>\n",
       "      <td>0.349004</td>\n",
       "      <td>0.259836</td>\n",
       "      <td>-0.043332</td>\n",
       "      <td>0.601670</td>\n",
       "      <td>0.190738</td>\n",
       "      <td>0.139108</td>\n",
       "      <td>0.037369</td>\n",
       "      <td>0.097188</td>\n",
       "      <td>0.117472</td>\n",
       "      <td>0.188598</td>\n",
       "      <td>0.165775</td>\n",
       "      <td>0.015261</td>\n",
       "      <td>0.129901</td>\n",
       "      <td>0.017794</td>\n",
       "      <td>0.026846</td>\n",
       "      <td>0.099141</td>\n",
       "      <td>0.094398</td>\n",
       "      <td>0.131175</td>\n",
       "      <td>0.098037</td>\n",
       "      <td>0.208856</td>\n",
       "      <td>0.305570</td>\n",
       "      <td>0.252523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good_for_kids</th>\n",
       "      <td>0.305284</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.271788</td>\n",
       "      <td>0.258887</td>\n",
       "      <td>0.205513</td>\n",
       "      <td>0.162469</td>\n",
       "      <td>-0.030382</td>\n",
       "      <td>0.318729</td>\n",
       "      <td>0.150360</td>\n",
       "      <td>0.055847</td>\n",
       "      <td>-0.079183</td>\n",
       "      <td>0.073806</td>\n",
       "      <td>0.060658</td>\n",
       "      <td>0.113262</td>\n",
       "      <td>0.083832</td>\n",
       "      <td>0.016557</td>\n",
       "      <td>0.045057</td>\n",
       "      <td>0.024901</td>\n",
       "      <td>0.040692</td>\n",
       "      <td>0.094233</td>\n",
       "      <td>0.068960</td>\n",
       "      <td>0.079808</td>\n",
       "      <td>0.121948</td>\n",
       "      <td>0.156536</td>\n",
       "      <td>0.291413</td>\n",
       "      <td>0.175058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_bike_parking</th>\n",
       "      <td>0.213318</td>\n",
       "      <td>0.271788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.235138</td>\n",
       "      <td>0.416044</td>\n",
       "      <td>0.155505</td>\n",
       "      <td>0.068084</td>\n",
       "      <td>0.160129</td>\n",
       "      <td>0.286298</td>\n",
       "      <td>-0.080443</td>\n",
       "      <td>-0.116295</td>\n",
       "      <td>0.130448</td>\n",
       "      <td>0.060595</td>\n",
       "      <td>0.114094</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>0.028307</td>\n",
       "      <td>-0.045849</td>\n",
       "      <td>0.018120</td>\n",
       "      <td>0.031203</td>\n",
       "      <td>0.083062</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>0.093579</td>\n",
       "      <td>0.144163</td>\n",
       "      <td>0.147115</td>\n",
       "      <td>0.180468</td>\n",
       "      <td>0.109552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>has_wifi</th>\n",
       "      <td>0.345032</td>\n",
       "      <td>0.258887</td>\n",
       "      <td>0.235138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.240796</td>\n",
       "      <td>0.195737</td>\n",
       "      <td>-0.039857</td>\n",
       "      <td>0.312217</td>\n",
       "      <td>0.155098</td>\n",
       "      <td>-0.034258</td>\n",
       "      <td>-0.037712</td>\n",
       "      <td>0.054699</td>\n",
       "      <td>0.082213</td>\n",
       "      <td>0.147320</td>\n",
       "      <td>0.120622</td>\n",
       "      <td>0.015937</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.023913</td>\n",
       "      <td>0.044006</td>\n",
       "      <td>0.082863</td>\n",
       "      <td>0.107467</td>\n",
       "      <td>0.126861</td>\n",
       "      <td>0.104742</td>\n",
       "      <td>0.173542</td>\n",
       "      <td>0.258938</td>\n",
       "      <td>0.210583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_range</th>\n",
       "      <td>0.349004</td>\n",
       "      <td>0.205513</td>\n",
       "      <td>0.416044</td>\n",
       "      <td>0.240796</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.148277</td>\n",
       "      <td>-0.052565</td>\n",
       "      <td>0.316105</td>\n",
       "      <td>0.400742</td>\n",
       "      <td>0.189623</td>\n",
       "      <td>0.003850</td>\n",
       "      <td>0.089349</td>\n",
       "      <td>0.073215</td>\n",
       "      <td>0.119422</td>\n",
       "      <td>0.098990</td>\n",
       "      <td>0.087231</td>\n",
       "      <td>0.176133</td>\n",
       "      <td>0.104221</td>\n",
       "      <td>0.122982</td>\n",
       "      <td>0.210487</td>\n",
       "      <td>0.057877</td>\n",
       "      <td>0.081321</td>\n",
       "      <td>0.129212</td>\n",
       "      <td>0.119632</td>\n",
       "      <td>0.170171</td>\n",
       "      <td>0.143570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>review_count</th>\n",
       "      <td>0.259836</td>\n",
       "      <td>0.162469</td>\n",
       "      <td>0.155505</td>\n",
       "      <td>0.195737</td>\n",
       "      <td>0.148277</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032413</td>\n",
       "      <td>0.187755</td>\n",
       "      <td>0.119984</td>\n",
       "      <td>0.010070</td>\n",
       "      <td>0.004748</td>\n",
       "      <td>0.076265</td>\n",
       "      <td>0.548164</td>\n",
       "      <td>0.860767</td>\n",
       "      <td>0.746949</td>\n",
       "      <td>0.026206</td>\n",
       "      <td>0.050451</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>-0.002576</td>\n",
       "      <td>0.014712</td>\n",
       "      <td>0.567452</td>\n",
       "      <td>0.699631</td>\n",
       "      <td>0.094133</td>\n",
       "      <td>0.844978</td>\n",
       "      <td>0.224983</td>\n",
       "      <td>0.610889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stars</th>\n",
       "      <td>-0.043332</td>\n",
       "      <td>-0.030382</td>\n",
       "      <td>0.068084</td>\n",
       "      <td>-0.039857</td>\n",
       "      <td>-0.052565</td>\n",
       "      <td>0.032413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.024486</td>\n",
       "      <td>0.037748</td>\n",
       "      <td>-0.125645</td>\n",
       "      <td>-0.277081</td>\n",
       "      <td>0.782187</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.043375</td>\n",
       "      <td>-0.000066</td>\n",
       "      <td>-0.007629</td>\n",
       "      <td>-0.038061</td>\n",
       "      <td>-0.031141</td>\n",
       "      <td>-0.066572</td>\n",
       "      <td>-0.064419</td>\n",
       "      <td>0.004130</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>-0.052899</td>\n",
       "      <td>0.014038</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>take_reservations</th>\n",
       "      <td>0.601670</td>\n",
       "      <td>0.318729</td>\n",
       "      <td>0.160129</td>\n",
       "      <td>0.312217</td>\n",
       "      <td>0.316105</td>\n",
       "      <td>0.187755</td>\n",
       "      <td>-0.024486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127941</td>\n",
       "      <td>0.064098</td>\n",
       "      <td>0.046331</td>\n",
       "      <td>0.086728</td>\n",
       "      <td>0.071131</td>\n",
       "      <td>0.129165</td>\n",
       "      <td>0.115583</td>\n",
       "      <td>-0.025522</td>\n",
       "      <td>0.048850</td>\n",
       "      <td>0.001131</td>\n",
       "      <td>0.010618</td>\n",
       "      <td>0.063990</td>\n",
       "      <td>0.053162</td>\n",
       "      <td>0.076183</td>\n",
       "      <td>0.084098</td>\n",
       "      <td>0.134832</td>\n",
       "      <td>0.282823</td>\n",
       "      <td>0.231242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>takes_credit_cards</th>\n",
       "      <td>0.190738</td>\n",
       "      <td>0.150360</td>\n",
       "      <td>0.286298</td>\n",
       "      <td>0.155098</td>\n",
       "      <td>0.400742</td>\n",
       "      <td>0.119984</td>\n",
       "      <td>0.037748</td>\n",
       "      <td>0.127941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.056399</td>\n",
       "      <td>-0.081383</td>\n",
       "      <td>0.084171</td>\n",
       "      <td>0.049945</td>\n",
       "      <td>0.079879</td>\n",
       "      <td>0.077804</td>\n",
       "      <td>0.027924</td>\n",
       "      <td>0.078443</td>\n",
       "      <td>-0.007124</td>\n",
       "      <td>-0.005260</td>\n",
       "      <td>0.009551</td>\n",
       "      <td>0.047402</td>\n",
       "      <td>0.055898</td>\n",
       "      <td>0.119925</td>\n",
       "      <td>0.097700</td>\n",
       "      <td>0.103271</td>\n",
       "      <td>0.073276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_review_age</th>\n",
       "      <td>0.139108</td>\n",
       "      <td>0.055847</td>\n",
       "      <td>-0.080443</td>\n",
       "      <td>-0.034258</td>\n",
       "      <td>0.189623</td>\n",
       "      <td>0.010070</td>\n",
       "      <td>-0.125645</td>\n",
       "      <td>0.064098</td>\n",
       "      <td>0.056399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.192355</td>\n",
       "      <td>0.003662</td>\n",
       "      <td>0.032199</td>\n",
       "      <td>0.031577</td>\n",
       "      <td>0.028122</td>\n",
       "      <td>0.218990</td>\n",
       "      <td>0.820888</td>\n",
       "      <td>0.243377</td>\n",
       "      <td>0.261623</td>\n",
       "      <td>0.377335</td>\n",
       "      <td>0.030324</td>\n",
       "      <td>0.035531</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>0.050846</td>\n",
       "      <td>-0.024121</td>\n",
       "      <td>-0.041140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_review_length</th>\n",
       "      <td>0.037369</td>\n",
       "      <td>-0.079183</td>\n",
       "      <td>-0.116295</td>\n",
       "      <td>-0.037712</td>\n",
       "      <td>0.003850</td>\n",
       "      <td>0.004748</td>\n",
       "      <td>-0.277081</td>\n",
       "      <td>0.046331</td>\n",
       "      <td>-0.081383</td>\n",
       "      <td>0.192355</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.133078</td>\n",
       "      <td>0.027662</td>\n",
       "      <td>0.027976</td>\n",
       "      <td>0.059979</td>\n",
       "      <td>0.083495</td>\n",
       "      <td>0.178872</td>\n",
       "      <td>0.112397</td>\n",
       "      <td>0.088888</td>\n",
       "      <td>0.168261</td>\n",
       "      <td>0.000789</td>\n",
       "      <td>0.004780</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>-0.004609</td>\n",
       "      <td>-0.016869</td>\n",
       "      <td>0.006024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_review_sentiment</th>\n",
       "      <td>0.097188</td>\n",
       "      <td>0.073806</td>\n",
       "      <td>0.130448</td>\n",
       "      <td>0.054699</td>\n",
       "      <td>0.089349</td>\n",
       "      <td>0.076265</td>\n",
       "      <td>0.782187</td>\n",
       "      <td>0.086728</td>\n",
       "      <td>0.084171</td>\n",
       "      <td>0.003662</td>\n",
       "      <td>-0.133078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026948</td>\n",
       "      <td>0.079057</td>\n",
       "      <td>0.035839</td>\n",
       "      <td>0.064738</td>\n",
       "      <td>0.083046</td>\n",
       "      <td>0.064385</td>\n",
       "      <td>0.045517</td>\n",
       "      <td>0.099804</td>\n",
       "      <td>0.025967</td>\n",
       "      <td>0.036676</td>\n",
       "      <td>-0.003620</td>\n",
       "      <td>0.056595</td>\n",
       "      <td>0.067912</td>\n",
       "      <td>0.044696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_funny_votes</th>\n",
       "      <td>0.117472</td>\n",
       "      <td>0.060658</td>\n",
       "      <td>0.060595</td>\n",
       "      <td>0.082213</td>\n",
       "      <td>0.073215</td>\n",
       "      <td>0.548164</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.071131</td>\n",
       "      <td>0.049945</td>\n",
       "      <td>0.032199</td>\n",
       "      <td>0.027662</td>\n",
       "      <td>0.026948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.725554</td>\n",
       "      <td>0.900795</td>\n",
       "      <td>0.045171</td>\n",
       "      <td>0.054688</td>\n",
       "      <td>0.028421</td>\n",
       "      <td>0.020624</td>\n",
       "      <td>0.034570</td>\n",
       "      <td>0.360497</td>\n",
       "      <td>0.444257</td>\n",
       "      <td>0.048625</td>\n",
       "      <td>0.507570</td>\n",
       "      <td>0.103491</td>\n",
       "      <td>0.325476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_cool_votes</th>\n",
       "      <td>0.188598</td>\n",
       "      <td>0.113262</td>\n",
       "      <td>0.114094</td>\n",
       "      <td>0.147320</td>\n",
       "      <td>0.119422</td>\n",
       "      <td>0.860767</td>\n",
       "      <td>0.043375</td>\n",
       "      <td>0.129165</td>\n",
       "      <td>0.079879</td>\n",
       "      <td>0.031577</td>\n",
       "      <td>0.027976</td>\n",
       "      <td>0.079057</td>\n",
       "      <td>0.725554</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.863073</td>\n",
       "      <td>0.077186</td>\n",
       "      <td>0.077668</td>\n",
       "      <td>0.050303</td>\n",
       "      <td>0.035827</td>\n",
       "      <td>0.061395</td>\n",
       "      <td>0.560654</td>\n",
       "      <td>0.684175</td>\n",
       "      <td>0.072980</td>\n",
       "      <td>0.777985</td>\n",
       "      <td>0.178104</td>\n",
       "      <td>0.554507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_useful_votes</th>\n",
       "      <td>0.165775</td>\n",
       "      <td>0.083832</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>0.120622</td>\n",
       "      <td>0.098990</td>\n",
       "      <td>0.746949</td>\n",
       "      <td>-0.000066</td>\n",
       "      <td>0.115583</td>\n",
       "      <td>0.077804</td>\n",
       "      <td>0.028122</td>\n",
       "      <td>0.059979</td>\n",
       "      <td>0.035839</td>\n",
       "      <td>0.900795</td>\n",
       "      <td>0.863073</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038893</td>\n",
       "      <td>0.061881</td>\n",
       "      <td>0.016645</td>\n",
       "      <td>0.006016</td>\n",
       "      <td>0.020459</td>\n",
       "      <td>0.450894</td>\n",
       "      <td>0.556973</td>\n",
       "      <td>0.091650</td>\n",
       "      <td>0.649913</td>\n",
       "      <td>0.149820</td>\n",
       "      <td>0.441297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_number_friends</th>\n",
       "      <td>0.015261</td>\n",
       "      <td>0.016557</td>\n",
       "      <td>0.028307</td>\n",
       "      <td>0.015937</td>\n",
       "      <td>0.087231</td>\n",
       "      <td>0.026206</td>\n",
       "      <td>-0.007629</td>\n",
       "      <td>-0.025522</td>\n",
       "      <td>0.027924</td>\n",
       "      <td>0.218990</td>\n",
       "      <td>0.083495</td>\n",
       "      <td>0.064738</td>\n",
       "      <td>0.045171</td>\n",
       "      <td>0.077186</td>\n",
       "      <td>0.038893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.315304</td>\n",
       "      <td>0.781161</td>\n",
       "      <td>0.545940</td>\n",
       "      <td>0.525380</td>\n",
       "      <td>0.053568</td>\n",
       "      <td>0.056955</td>\n",
       "      <td>0.045507</td>\n",
       "      <td>0.060506</td>\n",
       "      <td>0.004445</td>\n",
       "      <td>0.010809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_days_on_yelp</th>\n",
       "      <td>0.129901</td>\n",
       "      <td>0.045057</td>\n",
       "      <td>-0.045849</td>\n",
       "      <td>0.000448</td>\n",
       "      <td>0.176133</td>\n",
       "      <td>0.050451</td>\n",
       "      <td>-0.038061</td>\n",
       "      <td>0.048850</td>\n",
       "      <td>0.078443</td>\n",
       "      <td>0.820888</td>\n",
       "      <td>0.178872</td>\n",
       "      <td>0.083046</td>\n",
       "      <td>0.054688</td>\n",
       "      <td>0.077668</td>\n",
       "      <td>0.061881</td>\n",
       "      <td>0.315304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.320788</td>\n",
       "      <td>0.345481</td>\n",
       "      <td>0.467893</td>\n",
       "      <td>0.052168</td>\n",
       "      <td>0.060782</td>\n",
       "      <td>0.014544</td>\n",
       "      <td>0.078031</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>-0.006241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_number_fans</th>\n",
       "      <td>0.017794</td>\n",
       "      <td>0.024901</td>\n",
       "      <td>0.018120</td>\n",
       "      <td>0.023913</td>\n",
       "      <td>0.104221</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>-0.031141</td>\n",
       "      <td>0.001131</td>\n",
       "      <td>-0.007124</td>\n",
       "      <td>0.243377</td>\n",
       "      <td>0.112397</td>\n",
       "      <td>0.064385</td>\n",
       "      <td>0.028421</td>\n",
       "      <td>0.050303</td>\n",
       "      <td>0.016645</td>\n",
       "      <td>0.781161</td>\n",
       "      <td>0.320788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.798637</td>\n",
       "      <td>0.625891</td>\n",
       "      <td>0.029287</td>\n",
       "      <td>0.031803</td>\n",
       "      <td>0.030841</td>\n",
       "      <td>0.027903</td>\n",
       "      <td>0.002738</td>\n",
       "      <td>0.001965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_review_count</th>\n",
       "      <td>0.026846</td>\n",
       "      <td>0.040692</td>\n",
       "      <td>0.031203</td>\n",
       "      <td>0.044006</td>\n",
       "      <td>0.122982</td>\n",
       "      <td>-0.002576</td>\n",
       "      <td>-0.066572</td>\n",
       "      <td>0.010618</td>\n",
       "      <td>-0.005260</td>\n",
       "      <td>0.261623</td>\n",
       "      <td>0.088888</td>\n",
       "      <td>0.045517</td>\n",
       "      <td>0.020624</td>\n",
       "      <td>0.035827</td>\n",
       "      <td>0.006016</td>\n",
       "      <td>0.545940</td>\n",
       "      <td>0.345481</td>\n",
       "      <td>0.798637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.687701</td>\n",
       "      <td>0.029392</td>\n",
       "      <td>0.031895</td>\n",
       "      <td>0.032118</td>\n",
       "      <td>0.025542</td>\n",
       "      <td>0.004597</td>\n",
       "      <td>0.002460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_number_years_elite</th>\n",
       "      <td>0.099141</td>\n",
       "      <td>0.094233</td>\n",
       "      <td>0.083062</td>\n",
       "      <td>0.082863</td>\n",
       "      <td>0.210487</td>\n",
       "      <td>0.014712</td>\n",
       "      <td>-0.064419</td>\n",
       "      <td>0.063990</td>\n",
       "      <td>0.009551</td>\n",
       "      <td>0.377335</td>\n",
       "      <td>0.168261</td>\n",
       "      <td>0.099804</td>\n",
       "      <td>0.034570</td>\n",
       "      <td>0.061395</td>\n",
       "      <td>0.020459</td>\n",
       "      <td>0.525380</td>\n",
       "      <td>0.467893</td>\n",
       "      <td>0.625891</td>\n",
       "      <td>0.687701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045112</td>\n",
       "      <td>0.051960</td>\n",
       "      <td>0.059031</td>\n",
       "      <td>0.049284</td>\n",
       "      <td>0.035118</td>\n",
       "      <td>0.019713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday_checkins</th>\n",
       "      <td>0.094398</td>\n",
       "      <td>0.068960</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>0.107467</td>\n",
       "      <td>0.057877</td>\n",
       "      <td>0.567452</td>\n",
       "      <td>0.004130</td>\n",
       "      <td>0.053162</td>\n",
       "      <td>0.047402</td>\n",
       "      <td>0.030324</td>\n",
       "      <td>0.000789</td>\n",
       "      <td>0.025967</td>\n",
       "      <td>0.360497</td>\n",
       "      <td>0.560654</td>\n",
       "      <td>0.450894</td>\n",
       "      <td>0.053568</td>\n",
       "      <td>0.052168</td>\n",
       "      <td>0.029287</td>\n",
       "      <td>0.029392</td>\n",
       "      <td>0.045112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947118</td>\n",
       "      <td>0.039370</td>\n",
       "      <td>0.802160</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>0.262576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekend_checkins</th>\n",
       "      <td>0.131175</td>\n",
       "      <td>0.079808</td>\n",
       "      <td>0.093579</td>\n",
       "      <td>0.126861</td>\n",
       "      <td>0.081321</td>\n",
       "      <td>0.699631</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>0.076183</td>\n",
       "      <td>0.055898</td>\n",
       "      <td>0.035531</td>\n",
       "      <td>0.004780</td>\n",
       "      <td>0.036676</td>\n",
       "      <td>0.444257</td>\n",
       "      <td>0.684175</td>\n",
       "      <td>0.556973</td>\n",
       "      <td>0.056955</td>\n",
       "      <td>0.060782</td>\n",
       "      <td>0.031803</td>\n",
       "      <td>0.031895</td>\n",
       "      <td>0.051960</td>\n",
       "      <td>0.947118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042727</td>\n",
       "      <td>0.875169</td>\n",
       "      <td>0.109552</td>\n",
       "      <td>0.346862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_tip_length</th>\n",
       "      <td>0.098037</td>\n",
       "      <td>0.121948</td>\n",
       "      <td>0.144163</td>\n",
       "      <td>0.104742</td>\n",
       "      <td>0.129212</td>\n",
       "      <td>0.094133</td>\n",
       "      <td>-0.052899</td>\n",
       "      <td>0.084098</td>\n",
       "      <td>0.119925</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>0.013002</td>\n",
       "      <td>-0.003620</td>\n",
       "      <td>0.048625</td>\n",
       "      <td>0.072980</td>\n",
       "      <td>0.091650</td>\n",
       "      <td>0.045507</td>\n",
       "      <td>0.014544</td>\n",
       "      <td>0.030841</td>\n",
       "      <td>0.032118</td>\n",
       "      <td>0.059031</td>\n",
       "      <td>0.039370</td>\n",
       "      <td>0.042727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081828</td>\n",
       "      <td>0.081929</td>\n",
       "      <td>0.054535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_tips</th>\n",
       "      <td>0.208856</td>\n",
       "      <td>0.156536</td>\n",
       "      <td>0.147115</td>\n",
       "      <td>0.173542</td>\n",
       "      <td>0.119632</td>\n",
       "      <td>0.844978</td>\n",
       "      <td>0.014038</td>\n",
       "      <td>0.134832</td>\n",
       "      <td>0.097700</td>\n",
       "      <td>0.050846</td>\n",
       "      <td>-0.004609</td>\n",
       "      <td>0.056595</td>\n",
       "      <td>0.507570</td>\n",
       "      <td>0.777985</td>\n",
       "      <td>0.649913</td>\n",
       "      <td>0.060506</td>\n",
       "      <td>0.078031</td>\n",
       "      <td>0.027903</td>\n",
       "      <td>0.025542</td>\n",
       "      <td>0.049284</td>\n",
       "      <td>0.802160</td>\n",
       "      <td>0.875169</td>\n",
       "      <td>0.081828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.190528</td>\n",
       "      <td>0.450343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_caption_length</th>\n",
       "      <td>0.305570</td>\n",
       "      <td>0.291413</td>\n",
       "      <td>0.180468</td>\n",
       "      <td>0.258938</td>\n",
       "      <td>0.170171</td>\n",
       "      <td>0.224983</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.282823</td>\n",
       "      <td>0.103271</td>\n",
       "      <td>-0.024121</td>\n",
       "      <td>-0.016869</td>\n",
       "      <td>0.067912</td>\n",
       "      <td>0.103491</td>\n",
       "      <td>0.178104</td>\n",
       "      <td>0.149820</td>\n",
       "      <td>0.004445</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.002738</td>\n",
       "      <td>0.004597</td>\n",
       "      <td>0.035118</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>0.109552</td>\n",
       "      <td>0.081929</td>\n",
       "      <td>0.190528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.249235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_pics</th>\n",
       "      <td>0.252523</td>\n",
       "      <td>0.175058</td>\n",
       "      <td>0.109552</td>\n",
       "      <td>0.210583</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.610889</td>\n",
       "      <td>0.001727</td>\n",
       "      <td>0.231242</td>\n",
       "      <td>0.073276</td>\n",
       "      <td>-0.041140</td>\n",
       "      <td>0.006024</td>\n",
       "      <td>0.044696</td>\n",
       "      <td>0.325476</td>\n",
       "      <td>0.554507</td>\n",
       "      <td>0.441297</td>\n",
       "      <td>0.010809</td>\n",
       "      <td>-0.006241</td>\n",
       "      <td>0.001965</td>\n",
       "      <td>0.002460</td>\n",
       "      <td>0.019713</td>\n",
       "      <td>0.262576</td>\n",
       "      <td>0.346862</td>\n",
       "      <td>0.054535</td>\n",
       "      <td>0.450343</td>\n",
       "      <td>0.249235</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            alcohol?  good_for_kids  has_bike_parking  \\\n",
       "alcohol?                    1.000000       0.305284          0.213318   \n",
       "good_for_kids               0.305284       1.000000          0.271788   \n",
       "has_bike_parking            0.213318       0.271788          1.000000   \n",
       "has_wifi                    0.345032       0.258887          0.235138   \n",
       "price_range                 0.349004       0.205513          0.416044   \n",
       "review_count                0.259836       0.162469          0.155505   \n",
       "stars                      -0.043332      -0.030382          0.068084   \n",
       "take_reservations           0.601670       0.318729          0.160129   \n",
       "takes_credit_cards          0.190738       0.150360          0.286298   \n",
       "average_review_age          0.139108       0.055847         -0.080443   \n",
       "average_review_length       0.037369      -0.079183         -0.116295   \n",
       "average_review_sentiment    0.097188       0.073806          0.130448   \n",
       "number_funny_votes          0.117472       0.060658          0.060595   \n",
       "number_cool_votes           0.188598       0.113262          0.114094   \n",
       "number_useful_votes         0.165775       0.083832          0.094000   \n",
       "average_number_friends      0.015261       0.016557          0.028307   \n",
       "average_days_on_yelp        0.129901       0.045057         -0.045849   \n",
       "average_number_fans         0.017794       0.024901          0.018120   \n",
       "average_review_count        0.026846       0.040692          0.031203   \n",
       "average_number_years_elite  0.099141       0.094233          0.083062   \n",
       "weekday_checkins            0.094398       0.068960          0.082474   \n",
       "weekend_checkins            0.131175       0.079808          0.093579   \n",
       "average_tip_length          0.098037       0.121948          0.144163   \n",
       "number_tips                 0.208856       0.156536          0.147115   \n",
       "average_caption_length      0.305570       0.291413          0.180468   \n",
       "number_pics                 0.252523       0.175058          0.109552   \n",
       "\n",
       "                            has_wifi  price_range  review_count     stars  \\\n",
       "alcohol?                    0.345032     0.349004      0.259836 -0.043332   \n",
       "good_for_kids               0.258887     0.205513      0.162469 -0.030382   \n",
       "has_bike_parking            0.235138     0.416044      0.155505  0.068084   \n",
       "has_wifi                    1.000000     0.240796      0.195737 -0.039857   \n",
       "price_range                 0.240796     1.000000      0.148277 -0.052565   \n",
       "review_count                0.195737     0.148277      1.000000  0.032413   \n",
       "stars                      -0.039857    -0.052565      0.032413  1.000000   \n",
       "take_reservations           0.312217     0.316105      0.187755 -0.024486   \n",
       "takes_credit_cards          0.155098     0.400742      0.119984  0.037748   \n",
       "average_review_age         -0.034258     0.189623      0.010070 -0.125645   \n",
       "average_review_length      -0.037712     0.003850      0.004748 -0.277081   \n",
       "average_review_sentiment    0.054699     0.089349      0.076265  0.782187   \n",
       "number_funny_votes          0.082213     0.073215      0.548164  0.001320   \n",
       "number_cool_votes           0.147320     0.119422      0.860767  0.043375   \n",
       "number_useful_votes         0.120622     0.098990      0.746949 -0.000066   \n",
       "average_number_friends      0.015937     0.087231      0.026206 -0.007629   \n",
       "average_days_on_yelp        0.000448     0.176133      0.050451 -0.038061   \n",
       "average_number_fans         0.023913     0.104221      0.000474 -0.031141   \n",
       "average_review_count        0.044006     0.122982     -0.002576 -0.066572   \n",
       "average_number_years_elite  0.082863     0.210487      0.014712 -0.064419   \n",
       "weekday_checkins            0.107467     0.057877      0.567452  0.004130   \n",
       "weekend_checkins            0.126861     0.081321      0.699631  0.007863   \n",
       "average_tip_length          0.104742     0.129212      0.094133 -0.052899   \n",
       "number_tips                 0.173542     0.119632      0.844978  0.014038   \n",
       "average_caption_length      0.258938     0.170171      0.224983  0.000040   \n",
       "number_pics                 0.210583     0.143570      0.610889  0.001727   \n",
       "\n",
       "                            take_reservations  takes_credit_cards  \\\n",
       "alcohol?                             0.601670            0.190738   \n",
       "good_for_kids                        0.318729            0.150360   \n",
       "has_bike_parking                     0.160129            0.286298   \n",
       "has_wifi                             0.312217            0.155098   \n",
       "price_range                          0.316105            0.400742   \n",
       "review_count                         0.187755            0.119984   \n",
       "stars                               -0.024486            0.037748   \n",
       "take_reservations                    1.000000            0.127941   \n",
       "takes_credit_cards                   0.127941            1.000000   \n",
       "average_review_age                   0.064098            0.056399   \n",
       "average_review_length                0.046331           -0.081383   \n",
       "average_review_sentiment             0.086728            0.084171   \n",
       "number_funny_votes                   0.071131            0.049945   \n",
       "number_cool_votes                    0.129165            0.079879   \n",
       "number_useful_votes                  0.115583            0.077804   \n",
       "average_number_friends              -0.025522            0.027924   \n",
       "average_days_on_yelp                 0.048850            0.078443   \n",
       "average_number_fans                  0.001131           -0.007124   \n",
       "average_review_count                 0.010618           -0.005260   \n",
       "average_number_years_elite           0.063990            0.009551   \n",
       "weekday_checkins                     0.053162            0.047402   \n",
       "weekend_checkins                     0.076183            0.055898   \n",
       "average_tip_length                   0.084098            0.119925   \n",
       "number_tips                          0.134832            0.097700   \n",
       "average_caption_length               0.282823            0.103271   \n",
       "number_pics                          0.231242            0.073276   \n",
       "\n",
       "                            average_review_age  average_review_length  \\\n",
       "alcohol?                              0.139108               0.037369   \n",
       "good_for_kids                         0.055847              -0.079183   \n",
       "has_bike_parking                     -0.080443              -0.116295   \n",
       "has_wifi                             -0.034258              -0.037712   \n",
       "price_range                           0.189623               0.003850   \n",
       "review_count                          0.010070               0.004748   \n",
       "stars                                -0.125645              -0.277081   \n",
       "take_reservations                     0.064098               0.046331   \n",
       "takes_credit_cards                    0.056399              -0.081383   \n",
       "average_review_age                    1.000000               0.192355   \n",
       "average_review_length                 0.192355               1.000000   \n",
       "average_review_sentiment              0.003662              -0.133078   \n",
       "number_funny_votes                    0.032199               0.027662   \n",
       "number_cool_votes                     0.031577               0.027976   \n",
       "number_useful_votes                   0.028122               0.059979   \n",
       "average_number_friends                0.218990               0.083495   \n",
       "average_days_on_yelp                  0.820888               0.178872   \n",
       "average_number_fans                   0.243377               0.112397   \n",
       "average_review_count                  0.261623               0.088888   \n",
       "average_number_years_elite            0.377335               0.168261   \n",
       "weekday_checkins                      0.030324               0.000789   \n",
       "weekend_checkins                      0.035531               0.004780   \n",
       "average_tip_length                   -0.000525               0.013002   \n",
       "number_tips                           0.050846              -0.004609   \n",
       "average_caption_length               -0.024121              -0.016869   \n",
       "number_pics                          -0.041140               0.006024   \n",
       "\n",
       "                            average_review_sentiment  number_funny_votes  \\\n",
       "alcohol?                                    0.097188            0.117472   \n",
       "good_for_kids                               0.073806            0.060658   \n",
       "has_bike_parking                            0.130448            0.060595   \n",
       "has_wifi                                    0.054699            0.082213   \n",
       "price_range                                 0.089349            0.073215   \n",
       "review_count                                0.076265            0.548164   \n",
       "stars                                       0.782187            0.001320   \n",
       "take_reservations                           0.086728            0.071131   \n",
       "takes_credit_cards                          0.084171            0.049945   \n",
       "average_review_age                          0.003662            0.032199   \n",
       "average_review_length                      -0.133078            0.027662   \n",
       "average_review_sentiment                    1.000000            0.026948   \n",
       "number_funny_votes                          0.026948            1.000000   \n",
       "number_cool_votes                           0.079057            0.725554   \n",
       "number_useful_votes                         0.035839            0.900795   \n",
       "average_number_friends                      0.064738            0.045171   \n",
       "average_days_on_yelp                        0.083046            0.054688   \n",
       "average_number_fans                         0.064385            0.028421   \n",
       "average_review_count                        0.045517            0.020624   \n",
       "average_number_years_elite                  0.099804            0.034570   \n",
       "weekday_checkins                            0.025967            0.360497   \n",
       "weekend_checkins                            0.036676            0.444257   \n",
       "average_tip_length                         -0.003620            0.048625   \n",
       "number_tips                                 0.056595            0.507570   \n",
       "average_caption_length                      0.067912            0.103491   \n",
       "number_pics                                 0.044696            0.325476   \n",
       "\n",
       "                            number_cool_votes  number_useful_votes  \\\n",
       "alcohol?                             0.188598             0.165775   \n",
       "good_for_kids                        0.113262             0.083832   \n",
       "has_bike_parking                     0.114094             0.094000   \n",
       "has_wifi                             0.147320             0.120622   \n",
       "price_range                          0.119422             0.098990   \n",
       "review_count                         0.860767             0.746949   \n",
       "stars                                0.043375            -0.000066   \n",
       "take_reservations                    0.129165             0.115583   \n",
       "takes_credit_cards                   0.079879             0.077804   \n",
       "average_review_age                   0.031577             0.028122   \n",
       "average_review_length                0.027976             0.059979   \n",
       "average_review_sentiment             0.079057             0.035839   \n",
       "number_funny_votes                   0.725554             0.900795   \n",
       "number_cool_votes                    1.000000             0.863073   \n",
       "number_useful_votes                  0.863073             1.000000   \n",
       "average_number_friends               0.077186             0.038893   \n",
       "average_days_on_yelp                 0.077668             0.061881   \n",
       "average_number_fans                  0.050303             0.016645   \n",
       "average_review_count                 0.035827             0.006016   \n",
       "average_number_years_elite           0.061395             0.020459   \n",
       "weekday_checkins                     0.560654             0.450894   \n",
       "weekend_checkins                     0.684175             0.556973   \n",
       "average_tip_length                   0.072980             0.091650   \n",
       "number_tips                          0.777985             0.649913   \n",
       "average_caption_length               0.178104             0.149820   \n",
       "number_pics                          0.554507             0.441297   \n",
       "\n",
       "                            average_number_friends  average_days_on_yelp  \\\n",
       "alcohol?                                  0.015261              0.129901   \n",
       "good_for_kids                             0.016557              0.045057   \n",
       "has_bike_parking                          0.028307             -0.045849   \n",
       "has_wifi                                  0.015937              0.000448   \n",
       "price_range                               0.087231              0.176133   \n",
       "review_count                              0.026206              0.050451   \n",
       "stars                                    -0.007629             -0.038061   \n",
       "take_reservations                        -0.025522              0.048850   \n",
       "takes_credit_cards                        0.027924              0.078443   \n",
       "average_review_age                        0.218990              0.820888   \n",
       "average_review_length                     0.083495              0.178872   \n",
       "average_review_sentiment                  0.064738              0.083046   \n",
       "number_funny_votes                        0.045171              0.054688   \n",
       "number_cool_votes                         0.077186              0.077668   \n",
       "number_useful_votes                       0.038893              0.061881   \n",
       "average_number_friends                    1.000000              0.315304   \n",
       "average_days_on_yelp                      0.315304              1.000000   \n",
       "average_number_fans                       0.781161              0.320788   \n",
       "average_review_count                      0.545940              0.345481   \n",
       "average_number_years_elite                0.525380              0.467893   \n",
       "weekday_checkins                          0.053568              0.052168   \n",
       "weekend_checkins                          0.056955              0.060782   \n",
       "average_tip_length                        0.045507              0.014544   \n",
       "number_tips                               0.060506              0.078031   \n",
       "average_caption_length                    0.004445              0.000783   \n",
       "number_pics                               0.010809             -0.006241   \n",
       "\n",
       "                            average_number_fans  average_review_count  \\\n",
       "alcohol?                               0.017794              0.026846   \n",
       "good_for_kids                          0.024901              0.040692   \n",
       "has_bike_parking                       0.018120              0.031203   \n",
       "has_wifi                               0.023913              0.044006   \n",
       "price_range                            0.104221              0.122982   \n",
       "review_count                           0.000474             -0.002576   \n",
       "stars                                 -0.031141             -0.066572   \n",
       "take_reservations                      0.001131              0.010618   \n",
       "takes_credit_cards                    -0.007124             -0.005260   \n",
       "average_review_age                     0.243377              0.261623   \n",
       "average_review_length                  0.112397              0.088888   \n",
       "average_review_sentiment               0.064385              0.045517   \n",
       "number_funny_votes                     0.028421              0.020624   \n",
       "number_cool_votes                      0.050303              0.035827   \n",
       "number_useful_votes                    0.016645              0.006016   \n",
       "average_number_friends                 0.781161              0.545940   \n",
       "average_days_on_yelp                   0.320788              0.345481   \n",
       "average_number_fans                    1.000000              0.798637   \n",
       "average_review_count                   0.798637              1.000000   \n",
       "average_number_years_elite             0.625891              0.687701   \n",
       "weekday_checkins                       0.029287              0.029392   \n",
       "weekend_checkins                       0.031803              0.031895   \n",
       "average_tip_length                     0.030841              0.032118   \n",
       "number_tips                            0.027903              0.025542   \n",
       "average_caption_length                 0.002738              0.004597   \n",
       "number_pics                            0.001965              0.002460   \n",
       "\n",
       "                            average_number_years_elite  weekday_checkins  \\\n",
       "alcohol?                                      0.099141          0.094398   \n",
       "good_for_kids                                 0.094233          0.068960   \n",
       "has_bike_parking                              0.083062          0.082474   \n",
       "has_wifi                                      0.082863          0.107467   \n",
       "price_range                                   0.210487          0.057877   \n",
       "review_count                                  0.014712          0.567452   \n",
       "stars                                        -0.064419          0.004130   \n",
       "take_reservations                             0.063990          0.053162   \n",
       "takes_credit_cards                            0.009551          0.047402   \n",
       "average_review_age                            0.377335          0.030324   \n",
       "average_review_length                         0.168261          0.000789   \n",
       "average_review_sentiment                      0.099804          0.025967   \n",
       "number_funny_votes                            0.034570          0.360497   \n",
       "number_cool_votes                             0.061395          0.560654   \n",
       "number_useful_votes                           0.020459          0.450894   \n",
       "average_number_friends                        0.525380          0.053568   \n",
       "average_days_on_yelp                          0.467893          0.052168   \n",
       "average_number_fans                           0.625891          0.029287   \n",
       "average_review_count                          0.687701          0.029392   \n",
       "average_number_years_elite                    1.000000          0.045112   \n",
       "weekday_checkins                              0.045112          1.000000   \n",
       "weekend_checkins                              0.051960          0.947118   \n",
       "average_tip_length                            0.059031          0.039370   \n",
       "number_tips                                   0.049284          0.802160   \n",
       "average_caption_length                        0.035118          0.088600   \n",
       "number_pics                                   0.019713          0.262576   \n",
       "\n",
       "                            weekend_checkins  average_tip_length  number_tips  \\\n",
       "alcohol?                            0.131175            0.098037     0.208856   \n",
       "good_for_kids                       0.079808            0.121948     0.156536   \n",
       "has_bike_parking                    0.093579            0.144163     0.147115   \n",
       "has_wifi                            0.126861            0.104742     0.173542   \n",
       "price_range                         0.081321            0.129212     0.119632   \n",
       "review_count                        0.699631            0.094133     0.844978   \n",
       "stars                               0.007863           -0.052899     0.014038   \n",
       "take_reservations                   0.076183            0.084098     0.134832   \n",
       "takes_credit_cards                  0.055898            0.119925     0.097700   \n",
       "average_review_age                  0.035531           -0.000525     0.050846   \n",
       "average_review_length               0.004780            0.013002    -0.004609   \n",
       "average_review_sentiment            0.036676           -0.003620     0.056595   \n",
       "number_funny_votes                  0.444257            0.048625     0.507570   \n",
       "number_cool_votes                   0.684175            0.072980     0.777985   \n",
       "number_useful_votes                 0.556973            0.091650     0.649913   \n",
       "average_number_friends              0.056955            0.045507     0.060506   \n",
       "average_days_on_yelp                0.060782            0.014544     0.078031   \n",
       "average_number_fans                 0.031803            0.030841     0.027903   \n",
       "average_review_count                0.031895            0.032118     0.025542   \n",
       "average_number_years_elite          0.051960            0.059031     0.049284   \n",
       "weekday_checkins                    0.947118            0.039370     0.802160   \n",
       "weekend_checkins                    1.000000            0.042727     0.875169   \n",
       "average_tip_length                  0.042727            1.000000     0.081828   \n",
       "number_tips                         0.875169            0.081828     1.000000   \n",
       "average_caption_length              0.109552            0.081929     0.190528   \n",
       "number_pics                         0.346862            0.054535     0.450343   \n",
       "\n",
       "                            average_caption_length  number_pics  \n",
       "alcohol?                                  0.305570     0.252523  \n",
       "good_for_kids                             0.291413     0.175058  \n",
       "has_bike_parking                          0.180468     0.109552  \n",
       "has_wifi                                  0.258938     0.210583  \n",
       "price_range                               0.170171     0.143570  \n",
       "review_count                              0.224983     0.610889  \n",
       "stars                                     0.000040     0.001727  \n",
       "take_reservations                         0.282823     0.231242  \n",
       "takes_credit_cards                        0.103271     0.073276  \n",
       "average_review_age                       -0.024121    -0.041140  \n",
       "average_review_length                    -0.016869     0.006024  \n",
       "average_review_sentiment                  0.067912     0.044696  \n",
       "number_funny_votes                        0.103491     0.325476  \n",
       "number_cool_votes                         0.178104     0.554507  \n",
       "number_useful_votes                       0.149820     0.441297  \n",
       "average_number_friends                    0.004445     0.010809  \n",
       "average_days_on_yelp                      0.000783    -0.006241  \n",
       "average_number_fans                       0.002738     0.001965  \n",
       "average_review_count                      0.004597     0.002460  \n",
       "average_number_years_elite                0.035118     0.019713  \n",
       "weekday_checkins                          0.088600     0.262576  \n",
       "weekend_checkins                          0.109552     0.346862  \n",
       "average_tip_length                        0.081929     0.054535  \n",
       "number_tips                               0.190528     0.450343  \n",
       "average_caption_length                    1.000000     0.249235  \n",
       "number_pics                               0.249235     1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further visualize these relationships, we can plot certain features against our dependent variable, the Yelp rating. In the cell below we have provided the code to import Matplotlib. We can use Matplotlib's `.scatter()` method with the below syntax to plot what these correlations look like:\n",
    "\n",
    "```python\n",
    "plt.scatter(x_values_to_plot, y_values_to_plot, alpha=blending_val)\n",
    "```\n",
    "\n",
    "* `x_values_to_plot` are the values to be plotted along the x-axis\n",
    "* `y_values_to_plot` are the values to be plotted along the y-axis\n",
    "* `alpha=blending_val` is the blending value, or how transparent (0) or opaque (1) a plotted point is. This will help us distinguish areas of the plot with high point densities and low point densities\n",
    "\n",
    "Plot the three features that correlate most with Yelp rating (`average_review_sentiment`, `average_review_length`, `average_review_age`) against `stars`, our Yelp rating. Then plot a lowly correlating feature, such as `number_funny_votes`, against `stars`.\n",
    "\n",
    ">What is `average_review_sentiment`, you ask? `average_review_sentiment` is the average sentiment score for all reviews on a business' Yelp page. The sentiment score for a review was calculated using the sentiment analysis tool [VADER](https://github.com/cjhutto/vaderSentiment). VADER uses a labeled set of positive and negative words, along with codified rules of grammar, to estimate how positive or negative a statement is. Scores range from `-1`, most negative, to `+1`, most positive, with a score of `0` indicating a neutral statement. While not perfect, VADER does a good job at guessing the sentiment of text data!\n",
    "\n",
    "What kind of relationships do you see from the plots? Do you think these variables are good or bad features for our Yelp rating prediction model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# plot average_review_sentiment against stars here\n",
    "plt.scatter(df['average_review_sentiment'], df['stars'], alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot average_review_length against stars here\n",
    "plt.scatter(df['average_review_length'], df['stars'], alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot average_review_age against stars here\n",
    "plt.scatter(df['average_review_age'], df['stars'], alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot number_funny_votes against stars here\n",
    "plt.scatter(df['number_funny_votes'], df['stars'], alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why do you think `average_review_sentiment` correlates so well with Yelp rating?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Selection\n",
    "\n",
    "In order to put our data into a Linear Regression model, we need to separate out our features to model on and the Yelp ratings. From our correlation analysis we saw that the three features with the strongest correlations to Yelp rating are `average_review_sentiment`, `average_review_length`, and `average_review_age`. Since we want to dig a little deeper than `average_review_sentiment`, which understandably has a very high correlation with Yelp rating, let's choose to create our first model with `average_review_length` and `average_review_age` as features.\n",
    "\n",
    "Pandas lets us select one column of a DataFrame with the following syntax:\n",
    "\n",
    "```python\n",
    "subset_of_data = df['feature_to_select']\n",
    "```\n",
    "Pandas also lets us select multiple columns from a DataFrame with this syntax:\n",
    "\n",
    "```python\n",
    "subset_of_data = df[list_of_features_to_select]\n",
    "```\n",
    "Create a new DataFrame `features` that contains the columns we want to model on: `average_review_length` and `average_review_age`. Then create another DataFrame `ratings` that stores the value we want to predict, Yelp rating, or `stars` in `df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:57.953014Z",
     "start_time": "2018-09-14T14:51:57.945658Z"
    }
   },
   "outputs": [],
   "source": [
    "features = df[['average_review_length', 'average_review_age']]\n",
    "ratings = df['stars']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the Data into Training and Testing Sets\n",
    "\n",
    "We are just about ready to model! But first, we need to break our data into a training set and a test set so we can evaluate how well our model performs. We'll use scikit-learn's `train_test_split` function to do this split, which is provided in the cell below. This function takes two required parameters: the data, or our features, followed by our dependent variable, in our case the Yelp rating. Set the optional parameter `test_size` to be `0.2`. Finally, set the optional parameter `random_state` to `1`. This will make it so your data is split in the same way as the data in our solution code. \n",
    "\n",
    "Remember, this function returns 4 items in this order:\n",
    "1. The training data (features), which we can assign to `X_train`\n",
    "2. The testing data (features), which we can assign to `X_test`\n",
    "3. The training dependent variable (Yelp rating), which we can assign to `y_train`\n",
    "4. The testing dependent variable (Yelp rating), which we can assign to `y_test`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, ratings, \n",
    "                                                    test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create and Train the Model\n",
    "\n",
    "Now that our data is split into training and testing sets, we can finally model! In the cell below we have provided the code to import `LinearRegression` from scikit-learn's `linear_model` module. Create a new `LinearRegression` object named model. The `.fit()` method will fit our Linear Regression model to our training data and calculate the coefficients for our features. Call the `.fit()` method on `model` with `X_train` and `y_train` as parameters. Just like that our model has now been trained on our training data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate and Understand the Model\n",
    "\n",
    "Now we can evaluate our model in a variety of ways. The first way will be by using the `.score()` method, which provides the R^2 value for our model. Remember, R^2 is the coefficient of determination, or a measure of how much of the variance in our dependent variable, the predicted Yelp rating, is explained by our independent variables, our feature data. R^2 values range from `0` to `1`, with `0` indicating that the created model does not fit our data at all, and with `1` indicating the model perfectly fits our feature data. Call `.score()` on our model with `X_train` and `y_train` as parameters to calculate our training R^2 score. Then call `.score()` again on model with `X_test` and `y_test` as parameters to calculate R^2 for our testing data. What do these R^2 values say about our model? Do you think these features alone are able to effectively predict Yelp ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:58.631827Z",
     "start_time": "2018-09-14T14:51:58.619225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08196579189989794"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08316656582580495"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [2.9751703  3.88899485 3.58721119 3.28980423 3.88921865]\n",
      "Labels: [2.5, 4.5, 2.0, 4.0, 3.5]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "some_data = X_train.iloc[:5]\n",
    "some_labels = y_train.iloc[:5]\n",
    "pred = lin_reg.predict(some_data)\n",
    "print('Predictions:', pred)\n",
    "print('Labels:', list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg_predictions = lin_reg.predict(X_train)\n",
    "lin_reg_mse = mean_squared_error(y_train, lin_reg_predictions)\n",
    "lin_reg_rmse = np.sqrt(lin_reg_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9752104138784987"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After all that hard work, we can finally take a look at the coefficients on our different features! The model has an attribute `.coef_` which is an array of the feature coefficients determined by fitting our model to the training data. To make it easier for you to see which feature corresponds to which coefficient, we have provided some code in the cell that `zip`s together a list of our features with the coefficients and sorts them in descending order from most predictive to least predictive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('average_review_length', -0.000999130106549411),\n",
       " ('average_review_age', -0.00011539525908975403)]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(zip(['average_review_length','average_review_age'],lin_reg.coef_)),key = lambda x: abs(x[1]),reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly we can calculate the predicted Yelp ratings for our testing data and compare them to their actual Yelp ratings! Our model has a `.predict()` method which uses the model's coefficients to calculate the predicted Yelp rating. Call `.predict()` on `X_test` and assign the values to `y_predicted`. Use Matplotlib to plot `y_test` vs `y_predicted`. For a perfect linear regression model we would expect to see the data plotted along the line `y = x`, indicating homoscedasticity. Is this the case? If not, why not? Would you call this model heteroscedastic or homoscedastic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:51:58.656842Z",
     "start_time": "2018-09-14T14:51:58.653453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predicted = lin_reg.predict(X_test)\n",
    "\n",
    "plt.scatter(y_test, y_predicted, alpha=0.1)\n",
    "plt.xlabel('Yelp Rating')\n",
    "plt.ylabel('Predicted Yelp Rating')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Different Subsets of Data\n",
    "\n",
    "After evaluating the first model, you can see that `average_review_length` and `average_review_age` alone are not the best predictors for Yelp rating. Let's go do some more modeling with different subsets of features and see if we can achieve a more accurate model! In the cells below we have provided different lists of subsets of features that we will model with and evaluate. What other subsets of features would you want to test? Why do you think those feature sets are more predictive of Yelp rating than others? Create at least one more subset of features that you want to predict Yelp ratings from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset of only average review sentiment\n",
    "sentiment = ['average_review_sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset of all features that have a response range [0,1]\n",
    "binary_features = ['alcohol?','has_bike_parking','takes_credit_cards','good_for_kids','take_reservations','has_wifi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset of all features that vary on a greater range than [0,1]\n",
    "numeric_features = ['review_count','price_range','average_caption_length','number_pics','average_review_age','average_review_length','average_review_sentiment','number_funny_votes','number_cool_votes','number_useful_votes','average_tip_length','number_tips','average_number_friends','average_days_on_yelp','average_number_fans','average_review_count','average_number_years_elite','weekday_checkins','weekend_checkins']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all features\n",
    "all_features = binary_features + numeric_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add your own feature subset here\n",
    "feature_subset = ['weekend_checkins', 'weekday_checkins', 'average_review_sentiment', 'price_range', 'takes_credit_cards', 'number_pics', 'take_reservations', 'average_number_years_elite', 'has_bike_parking',  'alcohol?', 'good_for_kids', 'has_wifi', 'average_number_friends', 'number_funny_votes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have lists of different feature subsets, we can create new models from them. In order to more easily compare the performance of these new models, we have created a function for you below called `model_these_features()`. This function replicates the model building process you just completed with our first model! Take some time to review how the function works, analyzing it line by line. Fill in the empty comments with an explanation of the task the code beneath it is performing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take a list of features to model as a parameter\n",
    "def model_these_features(feature_list):\n",
    "    \n",
    "    # we create the data subsets with the rating column from the dataframe and the rature list that we provide\n",
    "    ratings = df.loc[:,'stars']\n",
    "    features = df.loc[:,feature_list]\n",
    "    \n",
    "    # performing train, test, split on the data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(features, ratings, test_size = 0.2, random_state = 1)\n",
    "    \n",
    "    # don't worry too much about these lines, just know that they allow the model to work when\n",
    "    # we model on just one feature instead of multiple features. Trust us on this one :)\n",
    "    if len(X_train.shape) < 2:\n",
    "        X_train = np.array(X_train).reshape(-1,1)\n",
    "        X_test = np.array(X_test).reshape(-1,1)\n",
    "    \n",
    "    # creating a linear regression model and training it on the data\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train,y_train)\n",
    "    \n",
    "    # printing the scores of the model\n",
    "    print('Train Score:', model.score(X_train,y_train))\n",
    "    print('Test Score:', model.score(X_test,y_test))\n",
    "    \n",
    "    # print the model features and their corresponding coefficients, from most predictive to least predictive\n",
    "    print(sorted(list(zip(feature_list,model.coef_)),key = lambda x: abs(x[1]),reverse=True))\n",
    "    \n",
    "    # predicting y values from our x test data\n",
    "    y_predicted = model.predict(X_test)\n",
    "    \n",
    "    # plotting the data (actual yelp rating vs our model's predicted rating)\n",
    "    plt.scatter(y_test,y_predicted, alpha=0.1)\n",
    "    plt.xlabel('Yelp Rating')\n",
    "    plt.ylabel('Predicted Yelp Rating')\n",
    "    plt.ylim(1,5)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.6118980950438655\n",
      "Test Score: 0.6114021046919492\n",
      "[('average_review_sentiment', 2.3033908433749612)]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_these_features(sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.012223180709591164\n",
      "Test Score: 0.010119542202269072\n",
      "[('has_bike_parking', 0.19003008208038988), ('alcohol?', -0.14549670708138188), ('has_wifi', -0.1318739757776259), ('good_for_kids', -0.08632485990337416), ('takes_credit_cards', 0.071755364921953), ('take_reservations', 0.04526558530451624)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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HVStvlI/86fMZf1GjlJbn6RXHLGZhZwvNJH9gNAMLO1t4xTGLc43ruBVzWTp3BoPDo+zYPcTg8ChL587guBVTd6lWE03tI+lK4IXAdSR9BK8AbiJ5xgAR8c4pi6YOPT090dt7wNzPdkC6t6+fjtbmvYanjQ25fdYU/hVyMMXVPzDE3dt3PtXx/7zFs5nZXso1rht/80e2bNvBDzdv47Fdw8ztaOFVRx/Kcw+dw0uek99lvnv7+rntvof5/p3befTJPczrbOPs4xaz+rAFubbXN2+9nx1P7ubffvcYOwaGmdPewslHzGVO5wzOOWFFbnEBPL5rkN/1PUH/wDAz21s4onsWXR2TuyYqaWNE9FTaVs+loSvS15gbJnV0O+BM1yG30zmuUmcbLy3rQxkcHqUp50sdM9tbWL1iPqc+++m4du0Zpjnnbqi2liZOPOIQXvycRU+VTYf26uoo0dnawnkvfnoGnZ27hyi15H9HfVdHKy/IcNaBCRNBRFya2dFtWpquQ24d1+Qc0T2L2+9/FID2UhMDQ6M8uWeI41fkO1XYdG2vVcvm8v+2JBMrd7Y18+SeEXYODPKy5y6a4J0HvqqXhiTdRY1nDEfEsVkFVYsvDTXGwNAIj/TvYc/wKG0tTcyf2TYtRjQ5rsmZiksKWZiu7fXgjt3c8cBjPL5riK6OEquWzZ0Wd/tPhVqXhmolgpoXxSLi/imIbdKcCMzMJm+/+gjKv+jTpLAyIn6cPm3soL8j2cysKOq5oextwHeAL6RFS6nykHkzMzvw1DPc4nzgFGAnQET8ljqeWWxmZgeGehLBnoh4av5hSS3U6EQuq9cu6VZJd0r6laSPVqgjSZ+VdI+kTemcRmZm1kBVE4GksW03SvogMEPSK4BvAz+oY997gNMj4jhgFfAqSSeNq3MGsDJ9rQU+P7nwzczsmap1RnC7pJOB9wN9wF3AXwHXABNOOheJscdcldLX+DOJs4HL0ro3A12S8r2f28ysYGqN/lkLfA64E3hfRHxxsjuX1EzyjOMjgX+OiFvGVVkCPFC2vjUt2z5uP2vTeFi+fPlkwzAzsxpqDR+9RdKJwDqgV9K18PQjquqZYygiRoBVkrqAKyQdHRGby6pUund7n/6HiFgPrIfkPoKJjmtmZvWb6H6AeSQTzvWR/GW/X88qjIjHJd0AvAooTwRbgWVl60uBbftzDDMz2z9VE4GkdcB/A/4X8Jcx0TSl+76/GxhKk8AM4OXA/xxX7SrgAknfBE4EdkTEdszMrGFqnRG8GDg5Ih7az30vBi5N+wmagG9FxIY0wRARF5N0PJ8J3APsAs7bz2OZmdl+qtVH8BfPZMcRsQk4vkL5xWXLQXLDmpmZ5STfidzNzCx3TgRmZgVXq7O45tMrIuLRqQ/HzMwarVZn8UaSMf0ClgOPpctdwO+Bw7MOzszMslf10lBEHB4RzwJ+BLwmIhZExHzgLOB7jQrQzMyyVU8fwQsj4pqxlYi4FnhJdiGZmVkj1fOksYcl/R1wOcmlojcCj2QalZmZNUw9ZwTnAt3AFemrOy0zM7ODwIRnBOnooAslzSybVtrMzA4S9Tyz+EWS7gbuTtePk/R/Mo/MzMwaop5LQ/8b+BPSfoGIuBM4LcugzMysceq6szgiHhhXNJJBLGZmloN6Rg09IOlFQEhqBd4JbMk2LDMza5R6zgjWkcwQuoTkQTKrgL/OMCYzM2uges4InjN+SmpJpwA/zyYkMzNrpHrOCD5XZ5mZmR2Aas0+ejLwIqBb0rvLNs0GmrMOzMzMGqPWpaFWYGZaZ1ZZ+U7gDVkGZWZmjVPrUZU3AjdK+mpE3D/ZHUtaBlwGLAJGgfUR8U/j6qwBvg/8R1r0vYj4+8key8zM9l89fQSXSOoaW5E0V9KP6njfMPCeiHgucBJwvqTnVaj3s4hYlb6cBMzMGqyeRLAgIh4fW4mIx4BDJnpTRGyPiNvS5SdI7j1Ysp9xmplZRupJBKOSlo+tSFpBMh113SQdBhwP3FJh88mS7pR0raTnV3n/Wkm9knr7+vomc2gzM5tAPfcR/C1wk6Qb0/XTgLX1HkDSTOC7wLsiYue4zbcBKyKiX9KZwJXAyvH7iIj1wHqAnp6eSSUhMzOrbcIzgoj4IbAa+BfgW8ALIqKePgIklUiSwNciYp/HW0bEzrGprdOnoJUkLZhE/GZm9gxVTQSSjkp/riZ5eP024A/A8rSsJkkCvgRsiYhPV6mzKK2HpBPSePz0MzOzBqp1aeg9wNuAT1XYFsDpE+z7FOBNwF2S7kjLPkiSVIiIi0nuR3i7pGFgN3BORPjSj5lZA+lA+97t6emJ3t7evMMwMzugSNoYET2VttWaYuJ1tXZa6Zq/mZkdeGpdGnpN+vMQkjmHfpKuvxS4AXAiMDM7CNSaYuI8AEkbgOdFxPZ0fTHwz40Jz8zMslbPDWWHjSWB1B+BZ2cUj5mZNVg9N5TdkM4t9A2S0ULnANdnGpWZmTXMhIkgIi6Q9KckdxRDMovoFdmGZWZmjVLPGQEkU0E8ERE/ltQhaVY6kZyZmR3gJuwjkPQ24DvAF9KiJSRzApmZ2UGgns7i80nuEt4JEBG/pY5pqM3M7MBQTyLYExGDYyuSWpjkNNRmZjZ91ZMIbpT0QWCGpFcA3wZ+kG1YZmbWKPUkgr8B+oC7gL8CrgH+LsugzMyscWqOGpLUBGyKiKOBLzYmJDMza6SaZwQRMQrcWf6oSjMzO7jUcx/BYuBXkm4FnhwrjIjXZhaVmZk1TD2J4KOZR2FmZrmp9TyCdmAdcCRJR/GXImK4UYGZmVlj1OojuBToIUkCZ1D5kZVmZnaAq5UInhcRb4yIL5A8W/jFk9mxpGWSrpe0RdKvJF1YoY4kfVbSPZI2SVo9yfjNzOwZqtVHMDS2EBHDkia772HgPRFxm6RZwEZJ10XE3WV1zgBWpq8Tgc+nP83MrEFqJYLjJO1Ml0VyZ/HOdDkiYnatHacPs9meLj8haQvJhHXlieBs4LKICOBmSV2SFo97EI6ZmWWo1qMqm6fqIJIOA44Hbhm3aQnwQNn61rRsr0QgaS2wFmD5ct/SYGY2leqZYuIZkTQT+C7wrojYOX5zhbfsM6FdRKyPiJ6I6Onu7s4iTDOzwso0EUgqkSSBr0XE9ypU2QosK1tfCmzLMiYzM9tbZolASe/yl4AtEfHpKtWuAt6cjh46Cdjh/gEzs8aq91GV++MU4E3AXZLuSMs+CCwHiIiLSWYyPRO4B9gFnJdhPGZmVkFmiSAibqJyH0B5nSB5ApqZmeUk885iMzOb3pwIzMwKzonAzKzgnAjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOCcCMzMCi7Lh9d/WdJDkjZX2b5G0g5Jd6SvD2UVi5mZVZflw+u/ClwEXFajzs8i4qwMYzAzswlkdkYQET8FHs1q/2ZmNjXy7iM4WdKdkq6V9PycYzEzK6QsLw1N5DZgRUT0SzoTuBJYWamipLXAWoDly5c3LEAzsyLI7YwgInZGRH+6fA1QkrSgSt31EdETET3d3d0NjdPM7GCXWyKQtEiS0uUT0lgeySseM7OiyuzSkKRvAGuABZK2Ah8GSgARcTHwBuDtkoaB3cA5ERFZxWNmZpVllggi4twJtl9EMrzUzMxylPeoITMzy5kTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFVxmiUDSlyU9JGlzle2S9FlJ90jaJGl1VrGYmVl1WZ4RfBV4VY3tZwAr09da4PMZxmJmZlVklggi4qfAozWqnA1cFombgS5Ji7OKx8zMKmvJ8dhLgAfK1remZdvHV5S0luSsAaBf0m/285gLgIf3871Zmq5xwfSNzXFNjuOanIMxrhXVNuSZCFShLCpVjIj1wPpnfECpNyJ6nul+ptp0jQumb2yOa3Ic1+QULa48Rw1tBZaVrS8FtuUUi5lZYeWZCK4C3pyOHjoJ2BER+1wWMjOzbGV2aUjSN4A1wAJJW4EPAyWAiLgYuAY4E7gH2AWcl1UsZZ7x5aWMTNe4YPrG5rgmx3FNTqHiUkTFy/JmZlYQvrPYzKzgnAjMzAruoEwE03V6izriWiNph6Q70teHGhDTMknXS9oi6VeSLqxQp+HtVWdcebRXu6RbJd2ZxvXRCnXyaK964mp4e5Udu1nS7ZI2VNiW23QzE8SVZ3vdJ+mu9Li9FbZPbZtFxEH3Ak4DVgObq2w/E7iW5F6Gk4Bbpklca4ANDW6rxcDqdHkW8O/A8/JurzrjyqO9BMxMl0vALcBJ06C96omr4e1Vdux3A1+vdPy8fh/riCvP9roPWFBj+5S22UF5RhDTdHqLOuJquIjYHhG3pctPAFtI7vAu1/D2qjOuhkvboD9dLaWv8SMu8miveuLKhaSlwKuBS6pUyeX3sY64prMpbbODMhHUodr0FtPByenp/bWSnt/IA0s6DDie5K/Jcrm2V424IIf2Si8n3AE8BFwXEdOiveqIC/L5fH0GeB8wWmV7Xp+vz1A7Lsjv9zGAf5W0UckUO+NNaZsVNRHUPb1Fg90GrIiI44DPAVc26sCSZgLfBd4VETvHb67wloa01wRx5dJeETESEatI7oY/QdLR46rk0l51xNXw9pJ0FvBQRGysVa1CWabtVWdcuf0+AqdExGqSWZrPl3TauO1T2mZFTQTTcnqLiNg5dnofEdcAJUkLsj6upBLJl+3XIuJ7Fark0l4TxZVXe5Ud/3HgBvadbj3Xz1e1uHJqr1OA10q6D/gmcLqky8fVyaO9Jowrz89XRGxLfz4EXAGcMK7KlLZZURPBtJzeQtIiSUqXTyD5/3kk42MK+BKwJSI+XaVaw9urnrhyaq9uSV3p8gzg5cCvx1XLo70mjCuP9oqID0TE0og4DDgH+ElEvHFctYa3Vz1x5dFe6bE6Jc0aWwZeCYwfaTilbZbn7KOZ0fSc3qKeuN4AvF3SMLAbOCfSIQIZOgV4E3BXen0Z4IPA8rK48miveuLKo70WA5dKaib5YvhWRGyQtK4srjzaq5648miviqZBe9UTV17ttRC4Is1BLcDXI+KHWbaZp5gwMyu4ol4aMjOzlBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgR3U0nHWN0k6o6zszyT9sELdNaowC+UE+x9RMkPkZkk/GBvLX6P+Kklnlq2/VtL7J3NMs6nmRGAHtXTc9zrg00qmau4E/jtw/hQdYndErIqIo0kmFJxov6tIxn+PxXdVRHxiimIx2y8H5Q1lZuUiYrOkHwB/A3QClwN/K+kYkt+Bj0TE98vfI+kjwBEkE3ktAz4ZEV+c4FD/Bhybvv8EkknNZpDcjHQe8B/A3wMzJJ0K/EO6vSciLpD0VWAn0AMsAt4XEd+R1ARcBLwk3UcT8OWI+M7+tolZOScCK4qPkkwiNghsIJlS4D+nl3JulfTjCu85lmSu907gdklXj80BM156R+/LSKbFgGR6h9MiYljSy4H/ERGvV/Jwk56IuCB931vH7WoxcCpwFMk0At8BXgccBhwDHEIyJfeXJ90CZlU4EVghRMSTkv4F6Af+DHiNpPemm9tJp64Y5/sRsRvYLel6kom/rhxXZ0Y6BcZhwEbgurR8DsmUDytJZoUs1RnqlRExCtwtaWFadirw7bT8wTQWsynjPgIrktH0JeD16bX9VRGxPCK2VKg/fv6VSvOx7E6nfl4BtPJ0H8HHgOvTvoPXkCSbeuwpW9a4n2aZcCKwIvoR8I6ymSWPr1Lv7LSDeT7JZIG/rLbDiNgBvBN4r5Lps+cAf0g3v7Ws6hMkj96cjJuA10tqSs8S1kzy/WY1ORFYEX2M5FLNJkmb0/VKbgWuBm4GPlatf2BMRNwO3EkyrfEngX+Q9HOguaza9cDz0iGnf15nvN8lmX9+M/AFkie17ajzvWYT8uyjZhWko4b6I+If844Fkie1RUR/enZyK8kTrB7MOy47OLiz2OzAsCEd4dRKcnbiJGBTxmcEZmYF5z4CM7OCcyIwMys4JwIzs4JzIjAzKzgnAjOzgvv/fb8ncrCyYV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_these_features(binary_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.6734992593766658\n",
      "Test Score: 0.6713318798120151\n",
      "[('average_review_sentiment', 2.2721076642095714), ('price_range', -0.08046080962701326), ('average_number_years_elite', -0.07190366288054215), ('average_caption_length', -0.00334706600778448), ('number_pics', -0.0029565028128954863), ('number_tips', -0.0015953050789045838), ('number_cool_votes', 0.0011468839227079775), ('average_number_fans', 0.0010510602097412254), ('average_review_length', -0.0005813655692093357), ('average_tip_length', -0.0005322032063459733), ('number_useful_votes', -0.0002320378475871201), ('average_review_count', -0.0002243170289501482), ('average_review_age', -0.00016930608165055923), ('average_days_on_yelp', 0.00012878025876674438), ('weekday_checkins', 5.91858075448613e-05), ('weekend_checkins', -5.518176206999151e-05), ('average_number_friends', 4.8269921116315975e-05), ('review_count', -3.483483763748981e-05), ('number_funny_votes', -7.884395673808679e-06)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_these_features(numeric_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.6807828861895334\n",
      "Test Score: 0.6782129045869247\n",
      "[('average_review_sentiment', 2.2808456996623825), ('alcohol?', -0.14991498593470284), ('has_wifi', -0.12155382629261363), ('good_for_kids', -0.1180781442201382), ('price_range', -0.06486730150043177), ('average_number_years_elite', -0.06278939713895364), ('has_bike_parking', 0.027296969912292805), ('takes_credit_cards', 0.024451837853653652), ('take_reservations', 0.014134559172969846), ('number_pics', -0.0013133612300796843), ('average_number_fans', 0.0010267986822656814), ('number_cool_votes', 0.0009723722734409323), ('number_tips', -0.0008546563320873921), ('average_caption_length', -0.0006472749798195219), ('average_review_length', -0.0005896257920272453), ('average_tip_length', -0.00042052175034042557), ('number_useful_votes', -0.00027150641256153645), ('average_review_count', -0.000233983569025111), ('average_review_age', -0.00015776544111324774), ('average_days_on_yelp', 0.0001232614766288456), ('review_count', 0.00010112259377362226), ('weekend_checkins', -9.239617469638006e-05), ('weekday_checkins', 6.153909123144103e-05), ('number_funny_votes', 4.8479351025075246e-05), ('average_number_friends', 2.069584037372081e-05)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_these_features(all_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.6518243929084266\n",
      "Test Score: 0.6510349767214891\n",
      "[('average_review_sentiment', 2.374372381671551), ('alcohol?', -0.19393380943949795), ('average_number_years_elite', -0.11314053316389265), ('good_for_kids', -0.10183447638456965), ('has_wifi', -0.09208620605853897), ('price_range', -0.07932558451735454), ('has_bike_parking', 0.07640925376490819), ('takes_credit_cards', 0.040573845008702085), ('take_reservations', -0.0221685023640667), ('number_pics', 0.0007683245041236105), ('average_number_friends', 7.482599687943659e-05), ('number_funny_votes', -3.281762239660253e-05), ('weekend_checkins', 2.9396899073695116e-05), ('weekday_checkins', -2.360095389072137e-05)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_these_features(feature_subset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you feel comfortable with the steps of the function, run models on the following subsets of data using `model_these_features()`:\n",
    "* `sentiment`: only `average_review_sentiment`\n",
    "* `binary_features`: all features that have a response range [0,1]\n",
    "* `numeric_features`: all features that vary on a greater range than [0,1]\n",
    "* `all_features`: all features\n",
    "* `feature_subset`: your own feature subset\n",
    "\n",
    "How does changing the feature sets affect the model's R^2 value? Which features are most important to predicting Yelp rating in the different models? Which models appear more or less homoscedastic?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Danielle's Delicious Delicacies' Debut\n",
    "\n",
    "You've loaded the data, cleaned it, modeled it, and evaluated it. You're tired, but glowing with pride after all the hard work. You close your eyes and can clearly see opening day of Danielle's Delicious Delicacies with a line out the door. But what will your Yelp rating be? Let's use our model to make a prediction.\n",
    "\n",
    "Our best model was the model using all features, so we'll work with this model again. In the cell below print `all_features` to get a reminder of what features we are working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-14T14:52:25.658575Z",
     "start_time": "2018-09-14T14:52:25.654756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['weekend_checkins',\n",
       " 'weekday_checkins',\n",
       " 'average_review_sentiment',\n",
       " 'price_range',\n",
       " 'takes_credit_cards',\n",
       " 'number_pics',\n",
       " 'take_reservations',\n",
       " 'average_number_years_elite',\n",
       " 'has_bike_parking',\n",
       " 'alcohol?',\n",
       " 'good_for_kids',\n",
       " 'has_wifi',\n",
       " 'average_number_friends',\n",
       " 'number_funny_votes']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to grab all the features and retrain our model on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.loc[:,feature_subset]\n",
    "ratings = df.loc[:,'stars']\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, ratings, test_size = 0.2, random_state = 1)\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To give you some perspective on the restaurants already out there, we have provided the mean, minimum, and maximum values for each feature below. Will Danielle's Delicious Delicacies be just another average restaurant, or will it be a 5 star behemoth amongst the masses?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Feature</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>weekend_checkins</td>\n",
       "      <td>49.612515</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>64647.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>weekday_checkins</td>\n",
       "      <td>45.385094</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>73830.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>average_review_sentiment</td>\n",
       "      <td>0.554935</td>\n",
       "      <td>-0.9952</td>\n",
       "      <td>0.996575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>price_range</td>\n",
       "      <td>1.035855</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>takes_credit_cards</td>\n",
       "      <td>0.700243</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>number_pics</td>\n",
       "      <td>1.489939</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>take_reservations</td>\n",
       "      <td>0.106086</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>average_number_years_elite</td>\n",
       "      <td>0.923313</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>10.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>has_bike_parking</td>\n",
       "      <td>0.350692</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>alcohol?</td>\n",
       "      <td>0.140610</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>good_for_kids</td>\n",
       "      <td>0.279029</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>has_wifi</td>\n",
       "      <td>0.134968</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>average_number_friends</td>\n",
       "      <td>105.132000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>4219.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>number_funny_votes</td>\n",
       "      <td>15.617091</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>36822.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Feature        Mean     Min           Max\n",
       "0             weekend_checkins   49.612515  0.0000  64647.000000\n",
       "1             weekday_checkins   45.385094  0.0000  73830.000000\n",
       "2     average_review_sentiment    0.554935 -0.9952      0.996575\n",
       "3                  price_range    1.035855  0.0000      4.000000\n",
       "4           takes_credit_cards    0.700243  0.0000      1.000000\n",
       "5                  number_pics    1.489939  0.0000   1150.000000\n",
       "6            take_reservations    0.106086  0.0000      1.000000\n",
       "7   average_number_years_elite    0.923313  0.0000     10.666667\n",
       "8             has_bike_parking    0.350692  0.0000      1.000000\n",
       "9                     alcohol?    0.140610  0.0000      1.000000\n",
       "10               good_for_kids    0.279029  0.0000      1.000000\n",
       "11                    has_wifi    0.134968  0.0000      1.000000\n",
       "12      average_number_friends  105.132000  1.0000   4219.000000\n",
       "13          number_funny_votes   15.617091  0.0000  36822.000000"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(features.columns,features.describe().loc['mean'],features.describe().loc['min'],features.describe().loc['max'])),columns=['Feature','Mean','Min','Max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on your plans for the restaurant, how you expect your customers to post on your Yelp page, and the values above, fill in the blanks in the NumPy array below with your desired values. The first blank corresponds with the feature at `index=0` in the DataFrame above, `alcohol?`, and the last blank corresponds to the feature at ``index=24``, `weekend_checkins`. Make sure to enter either `0` or `1` for all binary features, and if you aren't sure of what value to put for a feature, select the mean from the DataFrame above. After you enter the values, run the prediction cell below to receive your Yelp rating! How is Danielle's Delicious Delicacies debut going to be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-13T18:35:02.243266Z",
     "start_time": "2018-09-13T18:34:39.238Z"
    }
   },
   "outputs": [],
   "source": [
    "danielles_delicious_delicacies = np.array([48,40,0.5,1.1,1,15,1,0,1,0,1,1,100,10]).reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-13T18:35:02.244175Z",
     "start_time": "2018-09-13T18:34:39.239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.52502593])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(danielles_delicious_delicacies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "You have successfully built a linear regression model that predicts a restaurant's Yelp rating! As you have seen, it can be pretty hard to predict a rating like this even when we have a plethora of data. What other questions come to your mind when you see the data we have? What insights do you think could come from a different kind of analysis? Here are some ideas to ponder:\n",
    "\n",
    "* Can we predict the cuisine of a restaurant based on the users that review it?\n",
    "* What restaurants are similar to each other in ways besides cuisine?\n",
    "* Are there different restaurant vibes, and what kind of restaurants fit these conceptions?\n",
    "* How does social media status affect a restaurant's credibility and visibility?\n",
    "\n",
    "As you progress further into the field of data science, you will be able to create models that address these questions and many more! But in the meantime, get back to working on that burgeoning restaurant business plan."
   ]
  }
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