Capstone Project - The Battle of Neighborhoods where to startup-Portugal.ipynb

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    "# Capstone Project - The Battle of Neighborhoods (Week 2)\n",
    "### Applied Data Science Capstone by IBM/Coursera\n",
    "*Author: Henrique M. L. Pereira*\n",
    "\n",
    "This is the final part of Coursera Capstone Project. In this notebook I'll describe the problem at hand, \n",
    "explain why it is important, and explain how and where i'll retrieve the data needed to accomplish my objective.\n",
    "I am asked to use location data to explore geographical locations (New York, Toronto or another city of my choice)\n",
    "using Foursquare location data, to be creative and find a possible problem that can be solved with this approach."
   ]
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   "source": [
    "## Table of Contents\n",
    "* [Introduction](#introduction)\n",
    "* [Data and Methodology](#data)\n",
    "* [Data Exploration](#exploration)\n",
    "* [Results and Discussion](#discussion)\n",
    "* [Conclusion](#conclusion)"
   ]
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    "## 1. Introduction <a name=\"introduction\"></a>\n",
    "Everyone has faced or will face, some time of their life, a difficult decision to make: i.e. get or not get married, move\n",
    "away from your parent's house to your own place, have ou not have kids, enroll or not in that awesome course, etc...\n",
    "\n",
    "The situation I present might as well be a one of particular difficulty, mostly due to the challenge it may present. Let's\n",
    "imagine that you, as an entrepreneur, creative professional, and keen on producing innovative products, decide (along with\n",
    "some good and daring colleagues) to start a StartUp company. You are offered some help to establish an office in only 5 cities,\n",
    "all of them away from your hometown and also your colleagues' hometown.\n",
    "\n",
    "Let's define further our problem."
   ]
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    "### 1.1 Problem Definition <a name=\"problem\"></a>\n",
    "The 5 cities are major hubs in Portugal, diverse in all the ways, financial, economic, artistic, academic, full of opportunities, etc...\n",
    "The 5 cities were selected using data from the Nomad List, and are classified as good cities to establish a startup. These are 'LISBOA', 'PORTO', 'COIMBRA', 'BRAGA', 'AVEIRO'.\n",
    "\n",
    "Your StartUp, and it's work, may be defined by the following keywords:\n",
    "* Innovative;\n",
    "* Culturally impacting;\n",
    "* Environment friendly;\n",
    "* Information Technology;\n",
    "* Game changing;\n",
    "* Daring designs and approaches;\n",
    "\n",
    "You and your colleagues have the following concerns and needs (with no particular order):\n",
    "* Affordability of office and living;\n",
    "* Multicultural environment;\n",
    "* Near essential commodities;\n",
    "* Near places to relax and meet people\n",
    "* Always wanted to live in a buzzing city but without stressing situations;"
   ]
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    "### 1.2 Objective <a name=\"Objective\"></a>\n",
    "My objective is, with Data Science and Machine Learning, to advise the best city and neighborhood to set up said StartUp\n",
    "company based on the factors above specified, as well all the data from these cities that will be retrieved dinamically."
   ]
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    "## 2. Data <a name=\"data\"></a>\n",
    "Every data used in the project will be either stored in the resources folder of the GitHub repository associated with this project,\n",
    "or be available in the internet.\n",
    "\n",
    "I'll use the data related to the cities of Portugal from official Portuguese Government open source information. \n",
    "I'll also use data from the \"Nomad List\" ( https://nomadlist.com/ ) to rank the cities so I can put similarities and dissimilarities in perspective when comparing selected neighborhoods."
   ]
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     "text": [
      "Libraries imported...\n"
     ]
    }
   ],
   "source": [
    "# import of relevant packages\n",
    "import pandas as pd\n",
    "import urllib.request\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "import re\n",
    "from geopy.geocoders import Nominatim\n",
    "import geocoder\n",
    "import requests\n",
    "from bs4 import BeautifulSoup as bs\n",
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import folium\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "print('Libraries imported...')\n",
    "%matplotlib inline"
   ]
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    "### 2.1 Scrapping of data from Nomad List\n",
    "This data is intended to further help on selecting the best place for the startup. I'm expecting to have matches between\n",
    "neighborhoods of all cities, so this is the best objective way to select an appropriate neighborhood or at least the\n",
    "top best ones."
   ]
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     "text": [
      "Done!\n"
     ]
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   "source": [
    "# Define the list of Cities\n",
    "list_cities = ['lisbon', 'porto', 'coimbra', 'braga', 'aveiro']\n",
    "content = []\n",
    "col_data = []\n",
    "# Scrapping data\n",
    "try:\n",
    "    for i, c in enumerate(list_cities):\n",
    "        time.sleep(random.choice(np.linspace(1, 5))) # a random delayer to avoid blocking\n",
    "        req_parsed = bs(requests.get('https://nomadlist.com/{}'.format(c)).text, 'html.parser')\n",
    "        #Find relevant information and store it\n",
    "        table = req_parsed.find('table',{'class':'details'})\n",
    "        fullrows = []\n",
    "        for row in table.find_all('tr'):\n",
    "            col = row.find_all('td')\n",
    "            fullrows.append([row.text for row in col])\n",
    "            fullrows[-1][0] = re.sub('\\W', '', fullrows[-1][0])\n",
    "            fullrows[-1][1] = re.sub('\\W', ' ', fullrows[-1][1])\n",
    "        fullrows.append(['City', list_cities[i]])\n",
    "        row_data = np.array(fullrows).T.tolist()[1]\n",
    "        col_data = np.array(fullrows).T.tolist()[0]\n",
    "        fulldata = pd.DataFrame(row_data).T\n",
    "        fulldata.columns = col_data\n",
    "        content.append(fulldata)\n",
    "    print('Done!')\n",
    "except:\n",
    "    print('Something went wrong...')"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NomadScore</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Internet</th>\n",
       "      <th>Fun</th>\n",
       "      <th>Temperaturenow</th>\n",
       "      <th>Humiditynow</th>\n",
       "      <th>Airqualitynow</th>\n",
       "      <th>Safety</th>\n",
       "      <th>Qualityoflife</th>\n",
       "      <th>Peopledensity</th>\n",
       "      <th>Walkability</th>\n",
       "      <th>Peace</th>\n",
       "      <th>Trafficsafety</th>\n",
       "      <th>Hospitals</th>\n",
       "      <th>Happiness</th>\n",
       "      <th>Nightlife</th>\n",
       "      <th>FreeWiFiincity</th>\n",
       "      <th>Placestoworkfrom</th>\n",
       "      <th>ACorheating</th>\n",
       "      <th>Friendlytoforeigners</th>\n",
       "      <th>Englishspeaking</th>\n",
       "      <th>Freedomofspeech</th>\n",
       "      <th>Racialtolerance</th>\n",
       "      <th>Femalefriendly</th>\n",
       "      <th>LGBTfriendly</th>\n",
       "      <th>StartupScore</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4 12 5214 reviews</td>\n",
       "      <td>Affordable   2 312   mo</td>\n",
       "      <td>Fast  15Mbps  avg</td>\n",
       "      <td>Good</td>\n",
       "      <td>Perfect  26 C78 F</td>\n",
       "      <td>Humid  70</td>\n",
       "      <td>Great  22 µg m3</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Not busy  6k ppl km²  12x12m</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>lisbon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3 25 5202 reviews</td>\n",
       "      <td>Affordable   2 229   mo</td>\n",
       "      <td>Fast  18Mbps  avg</td>\n",
       "      <td>Good</td>\n",
       "      <td>Nice  21 C71 F</td>\n",
       "      <td>Way too humid  86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Good</td>\n",
       "      <td>Great</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>porto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2 13 5188 reviews</td>\n",
       "      <td>Too expensive   4 714   mo</td>\n",
       "      <td>Fast  18Mbps  avg</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Perfect  25 C77 F</td>\n",
       "      <td>Too humid  76</td>\n",
       "      <td>Great  16 µg m3</td>\n",
       "      <td>Good</td>\n",
       "      <td>Good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>Great</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Bad</td>\n",
       "      <td>coimbra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 17 5183 reviews</td>\n",
       "      <td>Affordable   1 803   mo</td>\n",
       "      <td>Fast  18Mbps  avg</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Perfect  26 C78 F</td>\n",
       "      <td>Too humid  78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>braga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2 96 5189 reviews</td>\n",
       "      <td>Affordable   2 437   mo</td>\n",
       "      <td>Fast  18Mbps  avg</td>\n",
       "      <td>Great</td>\n",
       "      <td>Perfect  22 C72 F</td>\n",
       "      <td>Too humid  85</td>\n",
       "      <td>Great  16 µg m3</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Bad</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Great</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Good</td>\n",
       "      <td>Okay</td>\n",
       "      <td>Okay</td>\n",
       "      <td>aveiro</td>\n",
       "    </tr>\n",
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       "          NomadScore                          Cost              Internet  \\\n",
       "0  4 12 5214 reviews       Affordable   2 312   mo    Fast  15Mbps  avg    \n",
       "1  3 25 5202 reviews       Affordable   2 229   mo    Fast  18Mbps  avg    \n",
       "2  2 13 5188 reviews    Too expensive   4 714   mo    Fast  18Mbps  avg    \n",
       "3  3 17 5183 reviews       Affordable   1 803   mo    Fast  18Mbps  avg    \n",
       "4  2 96 5189 reviews       Affordable   2 437   mo    Fast  18Mbps  avg    \n",
       "\n",
       "     Fun       Temperaturenow           Humiditynow      Airqualitynow Safety  \\\n",
       "0   Good    Perfect  26 C78 F            Humid  70     Great  22 µg m3   Good   \n",
       "1   Good       Nice  21 C71 F    Way too humid  86                 NaN   Good   \n",
       "2   Okay    Perfect  25 C77 F        Too humid  76     Great  16 µg m3   Good   \n",
       "3    Bad    Perfect  26 C78 F        Too humid  78                 NaN  Great   \n",
       "4  Great    Perfect  22 C72 F        Too humid  85     Great  16 µg m3  Great   \n",
       "\n",
       "  Qualityoflife                       Peopledensity Walkability Peace  \\\n",
       "0          Good       Not busy  6k ppl km²  12x12m        Great  Good   \n",
       "1          Good                                 NaN       Great  Good   \n",
       "2          Good                                 NaN       Great  Good   \n",
       "3          Good                                 NaN       Great  Good   \n",
       "4          Good                                 NaN       Great  Good   \n",
       "\n",
       "  Trafficsafety Hospitals Happiness Nightlife FreeWiFiincity Placestoworkfrom  \\\n",
       "0          Okay       Bad      Okay      Okay           Okay            Great   \n",
       "1          Okay       Bad      Okay      Okay           Good            Great   \n",
       "2          Okay       Bad      Okay      Okay           Okay            Great   \n",
       "3          Okay       Bad      Okay      Okay          Great            Great   \n",
       "4          Okay       Bad      Okay      Okay            Bad            Great   \n",
       "\n",
       "  ACorheating Friendlytoforeigners Englishspeaking Freedomofspeech  \\\n",
       "0        Okay                 Good            Good           Great   \n",
       "1       Great                 Good            Okay           Great   \n",
       "2       Great                 Good           Great           Great   \n",
       "3        Okay                Great            Okay           Great   \n",
       "4        Okay                Great            Okay           Great   \n",
       "\n",
       "  Racialtolerance Femalefriendly LGBTfriendly StartupScore     City  \n",
       "0            Okay           Good         Good         Okay   lisbon  \n",
       "1            Okay           Good         Okay         Okay    porto  \n",
       "2            Okay          Great          Bad          Bad  coimbra  \n",
       "3            Okay           Good         Okay         Okay    braga  \n",
       "4            Okay           Good         Okay         Okay   aveiro  "
      ]
     },
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   ],
   "source": [
    "# constructing final df\n",
    "NL_cities = pd.concat(content, axis=0, sort=False).reset_index(drop=True)\n",
    "NL_cities"
   ]
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    "# now some data clean up\n",
    "for col in NL_cities.columns:\n",
    "    NL_cities[col] = NL_cities[col].fillna(NL_cities[col].dropna(axis=0).mode()[0])\n",
    "\n",
    "corresp = {'Great':5, 'Good':4, 'Okay':3, 'Bad':1}\n",
    "\n",
    "NL_cities['NomadScore'] = NL_cities['NomadScore'].astype('str').\\\n",
    "    replace(r'\\s\\d+\\s\\w+$', '', regex=True).\\\n",
    "    replace(r'\\s', '.', regex=True).astype('float')\n",
    "\n",
    "NL_cities['Cost'] = NL_cities['Cost'].astype('str').\\\n",
    "    replace(r'[A-z]\\s*', '', regex=True).replace(r'\\s', '', regex=True).astype('int')\n",
    "\n",
    "NL_cities['Internet'] = NL_cities['Internet'].astype('str').\\\n",
    "    replace(r'[A-z]\\s*', '', regex=True).astype('int')\n",
    "\n",
    "NL_cities['Temperaturenow'] = NL_cities['Temperaturenow'].astype('str').\\\n",
    "    replace(r'[^0-9]', '', regex=True).\\\n",
    "    replace(r'[0-9]{2}$', '', regex=True).astype('int')\n",
    "\n",
    "NL_cities['Humiditynow'] = NL_cities['Humiditynow'].astype('str').\\\n",
    "    replace(r'[^0-9]', '', regex=True).\\\n",
    "    replace(r'[0-9]{1}$', '', regex=True).astype('int')\n",
    "     \n",
    "NL_cities['Airqualitynow'] = NL_cities['Airqualitynow'].astype('str').\\\n",
    "    replace(r'[^0-9]{2}.', '', regex=True).astype('float')\n",
    "\n",
    "list_cols = ['Fun','Safety', 'Qualityoflife', 'Walkability', \n",
    "           'Peace', 'Trafficsafety', 'Hospitals', \n",
    "           'Happiness', 'Nightlife', 'FreeWiFiincity', \n",
    "           'Placestoworkfrom', 'ACorheating', \n",
    "           'Friendlytoforeigners', 'Englishspeaking', \n",
    "           'Freedomofspeech', 'Racialtolerance', \n",
    "           'Femalefriendly', 'LGBTfriendly', 'StartupScore']\n",
    "for col in list_cols:\n",
    "    NL_cities[col] = NL_cities[col].replace(corresp).astype('int')\n",
    "\n",
    "NL_cities['Peopledensity'] = NL_cities['Peopledensity'].astype('str').\\\n",
    "    replace(r'k.*', '000', regex=True).replace(r'\\w*\\s+', '', regex=True).astype('int')\n",
    "\n",
    "NL_cities = NL_cities.rename(columns={'Cost':'CostPerMonth',\n",
    "                                      'Internet':'Internet_Mbps_avg',\n",
    "                                      'Weather':'WeatherCelsiusNow',\n",
    "                                      'Airqualitynow':'Airqualitynow_mcg_cubicm',\n",
    "                                      'Peopledensity':'Peopledensity_sqrKm'})\n",
    "\n",
    "cols = NL_cities.columns.tolist()\n",
    "cols = cols[-1:] + cols[:-1]\n",
    "NL_cities = NL_cities[cols]\n",
    "NL_cities['City'] = NL_cities['City'].str.upper().replace('LISBON', 'LISBOA') "
   ]
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   "source": [
    "Finally I have a clean and final Dataframe about all the cities."
   ]
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>NomadScore</th>\n",
       "      <th>CostPerMonth</th>\n",
       "      <th>Internet_Mbps_avg</th>\n",
       "      <th>Fun</th>\n",
       "      <th>Temperaturenow</th>\n",
       "      <th>Humiditynow</th>\n",
       "      <th>Airqualitynow_mcg_cubicm</th>\n",
       "      <th>Safety</th>\n",
       "      <th>Qualityoflife</th>\n",
       "      <th>Peopledensity_sqrKm</th>\n",
       "      <th>Walkability</th>\n",
       "      <th>Peace</th>\n",
       "      <th>Trafficsafety</th>\n",
       "      <th>Hospitals</th>\n",
       "      <th>Happiness</th>\n",
       "      <th>Nightlife</th>\n",
       "      <th>FreeWiFiincity</th>\n",
       "      <th>Placestoworkfrom</th>\n",
       "      <th>ACorheating</th>\n",
       "      <th>Friendlytoforeigners</th>\n",
       "      <th>Englishspeaking</th>\n",
       "      <th>Freedomofspeech</th>\n",
       "      <th>Racialtolerance</th>\n",
       "      <th>Femalefriendly</th>\n",
       "      <th>LGBTfriendly</th>\n",
       "      <th>StartupScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2312</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6000</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PORTO</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2229</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6000</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>2.13</td>\n",
       "      <td>4714</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6000</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1803</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6000</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AVEIRO</td>\n",
       "      <td>2.96</td>\n",
       "      <td>2437</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6000</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      City  NomadScore  CostPerMonth  Internet_Mbps_avg  Fun  Temperaturenow  \\\n",
       "0   LISBOA        4.12          2312                 15    4              26   \n",
       "1    PORTO        3.25          2229                 18    4              21   \n",
       "2  COIMBRA        2.13          4714                 18    3              25   \n",
       "3    BRAGA        3.17          1803                 18    1              26   \n",
       "4   AVEIRO        2.96          2437                 18    5              22   \n",
       "\n",
       "   Humiditynow  Airqualitynow_mcg_cubicm  Safety  Qualityoflife  \\\n",
       "0            7                      22.0       4              4   \n",
       "1            8                      16.0       4              4   \n",
       "2            7                      16.0       4              4   \n",
       "3            7                      16.0       5              4   \n",
       "4            8                      16.0       5              4   \n",
       "\n",
       "   Peopledensity_sqrKm  Walkability  Peace  Trafficsafety  Hospitals  \\\n",
       "0                 6000            5      4              3          1   \n",
       "1                 6000            5      4              3          1   \n",
       "2                 6000            5      4              3          1   \n",
       "3                 6000            5      4              3          1   \n",
       "4                 6000            5      4              3          1   \n",
       "\n",
       "   Happiness  Nightlife  FreeWiFiincity  Placestoworkfrom  ACorheating  \\\n",
       "0          3          3               3                 5            3   \n",
       "1          3          3               4                 5            5   \n",
       "2          3          3               3                 5            5   \n",
       "3          3          3               5                 5            3   \n",
       "4          3          3               1                 5            3   \n",
       "\n",
       "   Friendlytoforeigners  Englishspeaking  Freedomofspeech  Racialtolerance  \\\n",
       "0                     4                4                5                3   \n",
       "1                     4                3                5                3   \n",
       "2                     4                5                5                3   \n",
       "3                     5                3                5                3   \n",
       "4                     5                3                5                3   \n",
       "\n",
       "   Femalefriendly  LGBTfriendly  StartupScore  \n",
       "0               4             4             3  \n",
       "1               4             3             3  \n",
       "2               5             1             1  \n",
       "3               4             3             3  \n",
       "4               4             3             3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NL_cities.to_csv('resources_battle_n/NL_cities.csv')\n",
    "NL_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's scale data between 1 and 0 to use it further to rank these cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "NL_cities_norm = pd.DataFrame(scaler.fit(NL_cities.set_index('City')).transform(NL_cities.set_index('City')), columns=NL_cities.set_index('City').columns)\n",
    "NL_cities_norm['City'] = NL_cities['City']\n",
    "NL_cities_norm = NL_cities_norm.set_index('City').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>NomadScore</th>\n",
       "      <th>CostPerMonth</th>\n",
       "      <th>Internet_Mbps_avg</th>\n",
       "      <th>Fun</th>\n",
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       "      <th>Humiditynow</th>\n",
       "      <th>Airqualitynow_mcg_cubicm</th>\n",
       "      <th>Safety</th>\n",
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       "      <th>Walkability</th>\n",
       "      <th>Peace</th>\n",
       "      <th>Trafficsafety</th>\n",
       "      <th>Hospitals</th>\n",
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       "      <th>FreeWiFiincity</th>\n",
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       "      <th>Friendlytoforeigners</th>\n",
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       "      <td>0.5</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PORTO</td>\n",
       "      <td>0.562814</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>0.522613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AVEIRO</td>\n",
       "      <td>0.417085</td>\n",
       "      <td>0.217795</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.2</td>\n",
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      ],
      "text/plain": [
       "      City  NomadScore  CostPerMonth  Internet_Mbps_avg   Fun  Temperaturenow  \\\n",
       "0   LISBOA    1.000000      0.174854                0.0  0.75             1.0   \n",
       "1    PORTO    0.562814      0.146341                1.0  0.75             0.0   \n",
       "2  COIMBRA    0.000000      1.000000                1.0  0.50             0.8   \n",
       "3    BRAGA    0.522613      0.000000                1.0  0.00             1.0   \n",
       "4   AVEIRO    0.417085      0.217795                1.0  1.00             0.2   \n",
       "\n",
       "   Humiditynow  Airqualitynow_mcg_cubicm  Safety  Qualityoflife  \\\n",
       "0          0.0                       1.0     0.0            0.0   \n",
       "1          1.0                       0.0     0.0            0.0   \n",
       "2          0.0                       0.0     0.0            0.0   \n",
       "3          0.0                       0.0     1.0            0.0   \n",
       "4          1.0                       0.0     1.0            0.0   \n",
       "\n",
       "   Peopledensity_sqrKm  Walkability  Peace  Trafficsafety  Hospitals  \\\n",
       "0                  0.0          0.0    0.0            0.0        0.0   \n",
       "1                  0.0          0.0    0.0            0.0        0.0   \n",
       "2                  0.0          0.0    0.0            0.0        0.0   \n",
       "3                  0.0          0.0    0.0            0.0        0.0   \n",
       "4                  0.0          0.0    0.0            0.0        0.0   \n",
       "\n",
       "   Happiness  Nightlife  FreeWiFiincity  Placestoworkfrom  ACorheating  \\\n",
       "0        0.0        0.0            0.50               0.0          0.0   \n",
       "1        0.0        0.0            0.75               0.0          1.0   \n",
       "2        0.0        0.0            0.50               0.0          1.0   \n",
       "3        0.0        0.0            1.00               0.0          0.0   \n",
       "4        0.0        0.0            0.00               0.0          0.0   \n",
       "\n",
       "   Friendlytoforeigners  Englishspeaking  Freedomofspeech  Racialtolerance  \\\n",
       "0                   0.0              0.5              0.0              0.0   \n",
       "1                   0.0              0.0              0.0              0.0   \n",
       "2                   0.0              1.0              0.0              0.0   \n",
       "3                   1.0              0.0              0.0              0.0   \n",
       "4                   1.0              0.0              0.0              0.0   \n",
       "\n",
       "   Femalefriendly  LGBTfriendly  StartupScore  \n",
       "0             0.0      1.000000           1.0  \n",
       "1             0.0      0.666667           1.0  \n",
       "2             1.0      0.000000           0.0  \n",
       "3             0.0      0.666667           1.0  \n",
       "4             0.0      0.666667           1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NL_cities_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll remove now the variables that are not interesting to our startup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_remove = ['Internet_Mbps_avg','Nightlife','FreeWiFiincity','Placestoworkfrom','ACorheating','Temperaturenow','Humiditynow',\n",
    "                     'Airqualitynow_mcg_cubicm','Friendlytoforeigners','Englishspeaking','Freedomofspeech','Racialtolerance','Femalefriendly','LGBTfriendly',]\n",
    "NL_cities = NL_cities.drop(columns_to_remove, axis=1)\n",
    "NL_cities_norm = NL_cities_norm.drop(columns_to_remove, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 2.2 Loading of Portuguese cities neighborhood's and location data\n",
    "This data was retrieved from official Portuguese government urls (available at https://dados.gov.pt/). The precise url's are presented in the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3091 entries, 0 to 3090\n",
      "Data columns (total 21 columns):\n",
      "PartitionKey     3091 non-null object\n",
      "RowKey           3091 non-null object\n",
      "Timestamp        3091 non-null object\n",
      "areaha           3091 non-null object\n",
      "codigo           3091 non-null object\n",
      "codigoine        3091 non-null object\n",
      "codigopostal     3091 non-null object\n",
      "descrpostal      3091 non-null object\n",
      "eleitores2011    3091 non-null object\n",
      "email            3091 non-null object\n",
      "entidade         3091 non-null object\n",
      "entityid         3091 non-null object\n",
      "fax              3091 non-null object\n",
      "localidade       3091 non-null object\n",
      "nif              3091 non-null object\n",
      "nomecompleto     3091 non-null object\n",
      "populacao2011    3091 non-null object\n",
      "rua              3091 non-null object\n",
      "sitio            3091 non-null object\n",
      "telefone         3091 non-null object\n",
      "tipoentidade     3091 non-null object\n",
      "dtypes: object(21)\n",
      "memory usage: 507.2+ KB\n"
     ]
    }
   ],
   "source": [
    "column_names = ['City', 'Neighborhood', 'Latitude', 'Longitude']\n",
    "\n",
    "# We'll start by getting the data from all the cities of portugal\n",
    "pt_nbh_url = 'https://dados.gov.pt/pt/datasets/r/2266425a-18ca-44a8-8655-9c39624c0ccb'\n",
    "\n",
    "with urllib.request.urlopen(pt_nbh_url) as url:\n",
    "    pt_nbh_url = json.load(url)\n",
    "\n",
    "# Check the needed columns and relevant data\n",
    "json_normalize(pt_nbh_url['d']).info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "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>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>PC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Morreira e Trandeiras</td>\n",
       "      <td>4700-488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Ruilhe</td>\n",
       "      <td>4705-712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Escudeiros e Penso</td>\n",
       "      <td>4705-473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>Ceira</td>\n",
       "      <td>3030-853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Braga</td>\n",
       "      <td>4710-249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City           Neighborhood        PC\n",
       "55    BRAGA  Morreira e Trandeiras  4700-488\n",
       "70    BRAGA                 Ruilhe  4705-712\n",
       "35    BRAGA     Escudeiros e Penso  4705-473\n",
       "29  COIMBRA                  Ceira  3030-853\n",
       "18    BRAGA                  Braga  4710-249"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's save data from 'entidade' and 'codigopostal' to a Dataframe\n",
    "# I will want only the cities named in the introduction\n",
    "m_target = ['LISBOA', 'PORTO', 'COIMBRA', 'BRAGA', 'AVEIRO']\n",
    "PT_neighborhoods = pd.DataFrame(columns=['City', 'Neighborhood', 'PC'])\n",
    "\n",
    "for data in pt_nbh_url['d']:\n",
    "    city = re.sub('^(.)*(?=\\s\\(.)\\s\\(', '', # regex to clean the name\n",
    "                     data[u'entidade']).strip(')')\n",
    "    neighborhood_name = re.sub('\\s\\((.*)', '', # regex to clean the name of neighborhoods\n",
    "                               data[u'entidade'])\n",
    "    postalcode = data[u'codigopostal']\n",
    "\n",
    "    PT_neighborhoods = PT_neighborhoods.append({'City': city,\n",
    "                                          'Neighborhood': neighborhood_name,\n",
    "                                          'PC': postalcode}, ignore_index=True)\n",
    "\n",
    "PT_neighborhoods = PT_neighborhoods[PT_neighborhoods['City'].isin(m_target)].reset_index().iloc[:,1:]\n",
    "\n",
    "# Check of a sample from the resulting DataFrame\n",
    "PT_neighborhoods.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Now I build a function to extract and store coordinates in a DataFrame and by using the geocoder package to search for coordinates based on portuguese postal code\n",
    "def find_coordinates_pt(place, nbh, postal_code, df):\n",
    "    coordinates_df = pd.DataFrame(columns=('PC','Latitude', 'Longitude'))\n",
    "    print('Retrieving coordinates: ', end='')\n",
    "    \n",
    "    for p, n, post in zip(df[place], df[nbh], df[postal_code]): # For every line\n",
    "        try:\n",
    "            l = geocoder.arcgis(n + ', ' + p + ', ' + post + ', PT').latlng \n",
    "            coordinates_df = coordinates_df.append({\n",
    "                'PC':post,\n",
    "                'Latitude':l[0],\n",
    "                'Longitude':l[1]\n",
    "            }, ignore_index=True)\n",
    "            print(' .', end='')\n",
    "        except:\n",
    "            l = geocoder.arcgis(post + ', PT').latlng #\n",
    "            coordinates_df = coordinates_df.append({\n",
    "                'PC':post,\n",
    "                'Latitude':l[0],\n",
    "                'Longitude':l[1]\n",
    "            }, ignore_index=True)\n",
    "            print(' o', end='')\n",
    "    \n",
    "    print('End of function!')\n",
    "    \n",
    "    return pd.merge(df, coordinates_df, on=postal_code, how='inner') # merge of the 2 dataframes on the key 'postal code'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving coordinates:  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .End of function!\n"
     ]
    }
   ],
   "source": [
    "# Let's create our final dataset for the locations\n",
    "Neighborhoods = find_coordinates_pt('City', 'Neighborhood', 'PC', PT_neighborhoods).drop('PC', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now our dataframe is built with the cities we want, their neighborhoods and coordinates, so let's take a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "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>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Nogueira, Fraião e Lamaçães</td>\n",
       "      <td>41.545238</td>\n",
       "      <td>-8.395290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Lumiar</td>\n",
       "      <td>38.764110</td>\n",
       "      <td>-9.157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Celeirós, Aveleda e Vimieiro</td>\n",
       "      <td>41.514828</td>\n",
       "      <td>-8.455616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Escudeiros e Penso</td>\n",
       "      <td>41.480647</td>\n",
       "      <td>-8.424466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>Trouxemil e Torre de Vilela</td>\n",
       "      <td>40.273368</td>\n",
       "      <td>-8.447808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City                  Neighborhood   Latitude  Longitude\n",
       "56    BRAGA   Nogueira, Fraião e Lamaçães  41.545238  -8.395290\n",
       "49   LISBOA                        Lumiar  38.764110  -9.157000\n",
       "30    BRAGA  Celeirós, Aveleda e Vimieiro  41.514828  -8.455616\n",
       "35    BRAGA            Escudeiros e Penso  41.480647  -8.424466\n",
       "96  COIMBRA   Trouxemil e Torre de Vilela  40.273368  -8.447808"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Neighborhoods.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 0 Latitude values missing from Dataframe, and 0 Longitude values missing from Dataframe.\n"
     ]
    }
   ],
   "source": [
    "# Check if every location was returned a value of latitude and longitude\n",
    "print('Found', sum(Neighborhoods.Latitude.isna()), 'Latitude values missing from Dataframe, and',\n",
    "      sum(Neighborhoods.Longitude.isna()), 'Longitude values missing from Dataframe.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Save the dataframe as a csv file in the resources folder\n",
    "Neighborhoods.to_csv('resources_battle_n/Neighborhoods.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 2.3 Retrieval of Venue Data from FourSquare\n",
    "I'll retrieve the data of the Venues near each coordinate using the FourSquare API, and concatenate this information into another\n",
    "Dataframe.\n",
    "\n",
    "Now I'll define the required Foursquare Credentials and Version to use its request API url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "CLIENT_ID = '4QFEINHYB35HFDKHZQYHUSKX5UWGPTCFUCMXSIRHS5CQJJ51'\n",
    "CLIENT_SECRET = 'XUIM0KSEAAPNBMC0XXDK3QMS3XDYEHGRUG1VABVMKJMVTSCI'\n",
    "VERSION = '20190618'\n",
    "LIMIT = 100 # number of maximum venues to be returned for every neighborhood\n",
    "RADIUS = 500 # radius in meters to search for venues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "I'll create a function to repeat the process of pulling all venues for all the neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_nearby_venues(cities, names, latitudes, longitudes, radius=RADIUS, limit=LIMIT):\n",
    "    venues_list=[]\n",
    "    print('Retrieving venues: ', end='')\n",
    "    for city, name, lat, lng in zip(cities, names, latitudes, longitudes):\n",
    "        # build of the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            limit)\n",
    "        # make the GET request\n",
    "        try:\n",
    "            results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "            # return only relevant information for each nearby venue\n",
    "            venues_list.append([(\n",
    "                city,\n",
    "                name, \n",
    "                lat, \n",
    "                lng, \n",
    "                v['venue']['id'],\n",
    "                v['venue']['name'], \n",
    "                v['venue']['location']['lat'], \n",
    "                v['venue']['location']['lng'],  \n",
    "                v['venue']['categories'][0]['id'],  \n",
    "                v['venue']['categories'][0]['name']) for v in results])\n",
    "            print(' .', end='')\n",
    "        except KeyError:\n",
    "            print('KeyError... The API is not responding or max requests per day maxed out...')\n",
    "            break\n",
    "        \n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['City',\n",
    "                  'Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude',\n",
    "                  'VenueID',\n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude',\n",
    "                  'Venue CategoryID',\n",
    "                  'Venue Category']\n",
    "    \n",
    "    \n",
    "    # this will return the parent categories for each venue child category\n",
    "    url = 'https://api.foursquare.com/v2/venues/categories?&client_id={}&client_secret={}&v={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION,)\n",
    "\n",
    "    categories_results = requests.get(url).json()['response']['categories']\n",
    "    \n",
    "    fs_categories = json_normalize(data=categories_results, \n",
    "                               record_path='categories', \n",
    "                               record_prefix='child_', \n",
    "                               meta=['name'])[['name', 'child_name', 'child_id']]\n",
    "    \n",
    "    nearby_venues = pd.merge(nearby_venues, fs_categories, left_on='Venue CategoryID', right_on='child_id')\\\n",
    "        .drop(['Venue CategoryID', 'child_name', 'child_id'], axis=1)\\\n",
    "        .rename(columns={'name':'ParentCategory'})\n",
    "    \n",
    "    print('End of function!')\n",
    "    \n",
    "    return nearby_venues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll retrieve, with the above function, every venue for each neighborhood and \n",
    "create a new dataframe with this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving venues:  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .End of function!\n"
     ]
    }
   ],
   "source": [
    "all_venues = get_nearby_venues(\n",
    "                cities = Neighborhoods['City'],\n",
    "                names = Neighborhoods['Neighborhood'],\n",
    "                latitudes = Neighborhoods['Latitude'],\n",
    "                longitudes = Neighborhoods['Longitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting Dataframe was created and now I'll check its shape and information about the columns retrieved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1361, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_venues.to_csv('resources_battle_n/all_venues_retrieved.csv')\n",
    "all_venues.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1361 entries, 0 to 1360\n",
      "Data columns (total 10 columns):\n",
      "City                      1361 non-null object\n",
      "Neighborhood              1361 non-null object\n",
      "Neighborhood Latitude     1361 non-null float64\n",
      "Neighborhood Longitude    1361 non-null float64\n",
      "VenueID                   1361 non-null object\n",
      "Venue                     1361 non-null object\n",
      "Venue Latitude            1361 non-null float64\n",
      "Venue Longitude           1361 non-null float64\n",
      "Venue Category            1361 non-null object\n",
      "ParentCategory            1361 non-null object\n",
      "dtypes: float64(4), object(6)\n",
      "memory usage: 117.0+ KB\n"
     ]
    }
   ],
   "source": [
    "all_venues.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "all_venues = pd.read_csv('resources_battle_n/all_venues_retrieved.csv').iloc[:,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to check how many venues were retrived by city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City\n",
       "AVEIRO      43\n",
       "BRAGA      216\n",
       "COIMBRA    108\n",
       "LISBOA     842\n",
       "PORTO      152\n",
       "Name: Venue, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_venues.groupby(by='City').count()['Venue']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Methodology\n",
    "For this problem I will first make a data exploratory analisys in order to find hidden patterns among the data, and also check if all selected cities have venues that in some way are very different one from another.\n",
    "\n",
    "Next I'll apply 2 machine learning techniques (unsupervised), firstly K-Means, and OPTICS, in this order. My intention is to validade clusters found by k-means with the capability of finding outliers provided by OPTICS (in a similar fashion as DBSCAN)\n",
    "\n",
    "After selecting the best suited neighborhoods with the appropriate venues for our startup, I´ll use the data from Nomad List to rank these a little bit further to narrow down the eligible neighborhoods so that the clients can have an easy task selecting the best one to establish their startup.\n",
    "\n",
    "In all steps I'll try to make this notebook as dynamical and automated as possible, so the results can be repeated and even reflect changes in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Exploration <a name=\"exploration\"></a>\n",
    "Now that we have all the data gathered and cleaned we can start by exploring it and try to find patterns among all cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>VenueID</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "      <th>ParentCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Campo de Ourique</td>\n",
       "      <td>38.715810</td>\n",
       "      <td>-9.166920</td>\n",
       "      <td>4c3fff31d7fad13a966605da</td>\n",
       "      <td>Botica do Café</td>\n",
       "      <td>38.717435</td>\n",
       "      <td>-9.169567</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Braga</td>\n",
       "      <td>41.549397</td>\n",
       "      <td>-8.421888</td>\n",
       "      <td>4dcd47f5c65bdac71343861b</td>\n",
       "      <td>Theatro Circo Café</td>\n",
       "      <td>41.549403</td>\n",
       "      <td>-8.422619</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Nightlife Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Braga</td>\n",
       "      <td>41.549397</td>\n",
       "      <td>-8.421888</td>\n",
       "      <td>54e34a1a498ed36be4f558a8</td>\n",
       "      <td>Boutique do Leitão</td>\n",
       "      <td>41.551668</td>\n",
       "      <td>-8.426193</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>AVEIRO</td>\n",
       "      <td>Aradas</td>\n",
       "      <td>40.624324</td>\n",
       "      <td>-8.643784</td>\n",
       "      <td>4ba5f213f964a520a72a39e3</td>\n",
       "      <td>Litoralpan</td>\n",
       "      <td>40.625109</td>\n",
       "      <td>-8.646486</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Santo António</td>\n",
       "      <td>38.753610</td>\n",
       "      <td>-9.143020</td>\n",
       "      <td>4d8d22631d06b1f7fb072a3b</td>\n",
       "      <td>República Da Música</td>\n",
       "      <td>38.756093</td>\n",
       "      <td>-9.141956</td>\n",
       "      <td>Nightclub</td>\n",
       "      <td>Nightlife Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Ajuda</td>\n",
       "      <td>38.699740</td>\n",
       "      <td>-9.181180</td>\n",
       "      <td>5131ffdae4b02e87036444df</td>\n",
       "      <td>Bairro Arte</td>\n",
       "      <td>38.702617</td>\n",
       "      <td>-9.178510</td>\n",
       "      <td>Gift Shop</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Ajuda</td>\n",
       "      <td>38.699740</td>\n",
       "      <td>-9.181180</td>\n",
       "      <td>4ff6cdd3e4b002d4d335dd69</td>\n",
       "      <td>Mercearia Vencedora</td>\n",
       "      <td>38.699826</td>\n",
       "      <td>-9.177818</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Merelim</td>\n",
       "      <td>41.575830</td>\n",
       "      <td>-8.457731</td>\n",
       "      <td>4f6c4261e4b0a61998d20750</td>\n",
       "      <td>Belinha</td>\n",
       "      <td>41.579495</td>\n",
       "      <td>-8.454667</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>PORTO</td>\n",
       "      <td>Campanhã</td>\n",
       "      <td>41.148645</td>\n",
       "      <td>-8.580615</td>\n",
       "      <td>4d0d441ce0b98cfa3acbda93</td>\n",
       "      <td>Cafetaria d'Metro</td>\n",
       "      <td>41.149165</td>\n",
       "      <td>-8.586351</td>\n",
       "      <td>Café</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Alcântara</td>\n",
       "      <td>38.705055</td>\n",
       "      <td>-9.180971</td>\n",
       "      <td>4ddd5abfb3ad59fcbc58c0bc</td>\n",
       "      <td>Café Dias</td>\n",
       "      <td>38.702917</td>\n",
       "      <td>-9.184385</td>\n",
       "      <td>Café</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City      Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "899  LISBOA  Campo de Ourique              38.715810               -9.166920   \n",
       "748   BRAGA             Braga              41.549397               -8.421888   \n",
       "491   BRAGA             Braga              41.549397               -8.421888   \n",
       "359  AVEIRO            Aradas              40.624324               -8.643784   \n",
       "758  LISBOA     Santo António              38.753610               -9.143020   \n",
       "434  LISBOA             Ajuda              38.699740               -9.181180   \n",
       "445  LISBOA             Ajuda              38.699740               -9.181180   \n",
       "402   BRAGA           Merelim              41.575830               -8.457731   \n",
       "84    PORTO          Campanhã              41.148645               -8.580615   \n",
       "43   LISBOA         Alcântara              38.705055               -9.180971   \n",
       "\n",
       "                      VenueID                Venue  Venue Latitude  \\\n",
       "899  4c3fff31d7fad13a966605da       Botica do Café       38.717435   \n",
       "748  4dcd47f5c65bdac71343861b   Theatro Circo Café       41.549403   \n",
       "491  54e34a1a498ed36be4f558a8   Boutique do Leitão       41.551668   \n",
       "359  4ba5f213f964a520a72a39e3           Litoralpan       40.625109   \n",
       "758  4d8d22631d06b1f7fb072a3b  República Da Música       38.756093   \n",
       "434  5131ffdae4b02e87036444df          Bairro Arte       38.702617   \n",
       "445  4ff6cdd3e4b002d4d335dd69  Mercearia Vencedora       38.699826   \n",
       "402  4f6c4261e4b0a61998d20750              Belinha       41.579495   \n",
       "84   4d0d441ce0b98cfa3acbda93    Cafetaria d'Metro       41.149165   \n",
       "43   4ddd5abfb3ad59fcbc58c0bc            Café Dias       38.702917   \n",
       "\n",
       "     Venue Longitude Venue Category  ParentCategory  \n",
       "899        -9.169567    Coffee Shop            Food  \n",
       "748        -8.422619      Nightclub  Nightlife Spot  \n",
       "491        -8.426193     Restaurant            Food  \n",
       "359        -8.646486         Bakery            Food  \n",
       "758        -9.141956      Nightclub  Nightlife Spot  \n",
       "434        -9.178510      Gift Shop  Shop & Service  \n",
       "445        -9.177818     Restaurant            Food  \n",
       "402        -8.454667         Bakery            Food  \n",
       "84         -8.586351           Café            Food  \n",
       "43         -9.184385           Café            Food  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_venues.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 139 unique Venue categories and a total of 8 Parent unique Venue categories\n"
     ]
    }
   ],
   "source": [
    "print('We have {} unique Venue categories'.format(len(all_venues['Venue Category'].unique())), \n",
    "     'and a total of {} Parent unique Venue categories'.format(len(all_venues['ParentCategory'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we have too many unique Venue Categories. I might have to opt for using the Parent Categories in order to prevent the 'curse of dimensionality' when applying unsupervised learnig...\n",
    "\n",
    "Either way, I'll look into the patern in a graphical way. First, the 10 top most frequent Venue Categpries found within our Neighborhoods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 10 venues retrieved\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "sb.set_style('whitegrid')\n",
    "ax = sb.barplot(data=all_venues.groupby(by=['Venue Category']).count().sort_values(by='Venue', ascending=False).reset_index()[:20],\n",
    "          x='Venue Category',\n",
    "          y='Venue', palette='deep')\n",
    "plt.xticks(rotation=90)\n",
    "ax.set_title('Top 20 venues retrieved', fontsize=20)\n",
    "ax.set_ylabel('Nr of Venues')\n",
    "sb.despine(top=True, bottom=True, right=True)\n",
    "plt.tight_layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Appart from the 'Hotel', 'Plaza' and 'Park' and some others, every other category is food related. So I'll look into the Parent Categories now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = sb.boxplot(data=all_venues.groupby(by=['City','ParentCategory']).count().sort_values(by='Venue', ascending=False).reset_index(),\n",
    "          x='ParentCategory',\n",
    "          y='Venue', palette='deep')\n",
    "plt.xticks(rotation=45)\n",
    "ax.set_title('Parent venues distributions retrieved', fontsize=20)\n",
    "ax.set_ylabel('Nr of Venues')\n",
    "sb.despine(top=True, bottom=True, right=True)\n",
    "plt.tight_layout\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems best to use the Parent Categories. I only have 8 Categories and, besides the clear pattern of the food Category being the most frequent, I'll make more use of this level of information to better classify\n",
    "any cluster returned by unsupervised learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax = sb.barplot(data=all_venues.groupby(by=['City','ParentCategory']).count().sort_values(by=['Venue'], ascending=[False]).reset_index(),\n",
    "          hue='ParentCategory',\n",
    "          x='City',\n",
    "          y='Venue', palette='deep')\n",
    "ax.set_title('Parent venues distributions retrieved', fontsize=20)\n",
    "ax.set_ylabel('Nr of Venues')\n",
    "sb.despine(top=True, bottom=True, right=True)\n",
    "plt.tight_layout\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like every city is a bit diferent from each other. Food Venues are the mode for each of them, only changing the proportion of other venues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll encode the Parent Categories into dummy variables for each Neighborhood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1361 entries, 0 to 1360\n",
      "Data columns (total 12 columns):\n",
      "Arts & Entertainment           1361 non-null uint8\n",
      "College & University           1361 non-null uint8\n",
      "Food                           1361 non-null uint8\n",
      "Nightlife Spot                 1361 non-null uint8\n",
      "Outdoors & Recreation          1361 non-null uint8\n",
      "Professional & Other Places    1361 non-null uint8\n",
      "Shop & Service                 1361 non-null uint8\n",
      "Travel & Transport             1361 non-null uint8\n",
      "City                           1361 non-null object\n",
      "Neighborhood                   1361 non-null object\n",
      "Neighborhood Latitude          1361 non-null float64\n",
      "Neighborhood Longitude         1361 non-null float64\n",
      "dtypes: float64(2), object(2), uint8(8)\n",
      "memory usage: 53.2+ KB\n"
     ]
    }
   ],
   "source": [
    "list_City_adresses = all_venues['City'].unique().tolist()\n",
    "cities_dp = pd.get_dummies(all_venues[['ParentCategory']], prefix=\"\", prefix_sep=\"\")\n",
    "cities_dp[['City', 'Neighborhood','Neighborhood Latitude', 'Neighborhood Longitude']] = all_venues[['City', 'Neighborhood','Neighborhood Latitude', 'Neighborhood Longitude']]\n",
    "cities_dp.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now I'll find the average of the distribution of categories for each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>College &amp; University</th>\n",
       "      <th>Food</th>\n",
       "      <th>Nightlife Spot</th>\n",
       "      <th>Outdoors &amp; Recreation</th>\n",
       "      <th>Professional &amp; Other Places</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Travel &amp; Transport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Mire de Tibães</td>\n",
       "      <td>41.565894</td>\n",
       "      <td>-8.478608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>PORTO</td>\n",
       "      <td>Aldoar, Foz do Douro e Nevogilde</td>\n",
       "      <td>41.166173</td>\n",
       "      <td>-8.678074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Benfica</td>\n",
       "      <td>38.750410</td>\n",
       "      <td>-9.210688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Priscos</td>\n",
       "      <td>41.490921</td>\n",
       "      <td>-8.469728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AVEIRO</td>\n",
       "      <td>Oliveirinha</td>\n",
       "      <td>40.607447</td>\n",
       "      <td>-8.611250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      City                      Neighborhood  Neighborhood Latitude  \\\n",
       "24   BRAGA                    Mire de Tibães              41.565894   \n",
       "70   PORTO  Aldoar, Foz do Douro e Nevogilde              41.166173   \n",
       "54  LISBOA                           Benfica              38.750410   \n",
       "29   BRAGA                           Priscos              41.490921   \n",
       "3   AVEIRO                       Oliveirinha              40.607447   \n",
       "\n",
       "    Neighborhood Longitude  Arts & Entertainment  College & University  \\\n",
       "24               -8.478608                   0.0                   0.0   \n",
       "70               -8.678074                   0.0                   0.0   \n",
       "54               -9.210688                   0.0                   0.0   \n",
       "29               -8.469728                   0.0                   0.0   \n",
       "3                -8.611250                   0.0                   0.0   \n",
       "\n",
       "        Food  Nightlife Spot  Outdoors & Recreation  \\\n",
       "24  1.000000        0.000000               0.000000   \n",
       "70  0.333333        0.333333               0.333333   \n",
       "54  0.400000        0.000000               0.200000   \n",
       "29  1.000000        0.000000               0.000000   \n",
       "3   0.000000        0.000000               0.000000   \n",
       "\n",
       "    Professional & Other Places  Shop & Service  Travel & Transport  \n",
       "24                          0.0             0.0                 0.0  \n",
       "70                          0.0             0.0                 0.0  \n",
       "54                          0.0             0.3                 0.1  \n",
       "29                          0.0             0.0                 0.0  \n",
       "3                           0.0             1.0                 0.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_g_avg = cities_dp.groupby(by=['City', 'Neighborhood','Neighborhood Latitude', 'Neighborhood Longitude']).mean().reset_index()\n",
    "cities_g_avg.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Analysis of data <a name=\"analysis\"></a>\n",
    "Now with all the data retrieved I will aplly unsupervised learning to identify clusters with similar kinds of venues per Neighborhood."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 K-Means method <a name=\"kmeans\"></a>\n",
    "In order to achieve good clustering, first I need to fin the optimal number of clusters to do that.\n",
    "\n",
    "In this stage I'm going to try to identify the right number of clusters based on the results of the K-means inertia graph (elbow method)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_elbow_kmeans(df, max_ks=7):\n",
    "    ks = np.arange(1, max_ks+1)\n",
    "    inertias = []\n",
    "    \n",
    "    for k in ks:\n",
    "        # Create a KMeans instance with k clusters: model\n",
    "        t_model = KMeans(n_clusters=k, n_jobs=-1, n_init=30, random_state=123)\n",
    "        # Fit model to samples\n",
    "        t_model.fit(df.iloc[:,4:])\n",
    "        # Append the inertia to the list of inertias\n",
    "        inertias.append(t_model.inertia_)\n",
    "    \n",
    "    # Calculate the distance between line k1 to kmax and ki cluster\n",
    "    x1, y1 = 1, inertias[0]\n",
    "    x2, y2 = max_ks, inertias[-1]\n",
    "\n",
    "    distances = []\n",
    "    for i, v in enumerate(inertias):\n",
    "        x0 = i+1\n",
    "        y0 = v\n",
    "        numerator = abs((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1)\n",
    "        denominator = np.sqrt((y2 - y1)**2 + (x2 - x1)**2)\n",
    "        distances.append(numerator/denominator)\n",
    "        \n",
    "    df = pd.concat([pd.Series(ks, name='ks'), pd.Series(inertias, name='Inertia'), pd.Series(distances, name='Distance')], axis=1).set_index('ks')\n",
    "    \n",
    "    xmax = df['Distance'].idxmax()\n",
    "    ymax = df['Distance'].max()\n",
    "    dmax = df['Inertia'].loc[xmax]\n",
    "    \n",
    "    # Plot ks (x-axis) vs inertias (y-axis) using plt.plot(). \n",
    "    fig = plt.figure(figsize=(10,5))\n",
    "    ax = sb.lineplot(data=df)\n",
    "    plt.axvline(xmax, c='r', ls=':')\n",
    "    \n",
    "    ax.annotate('Max Distance', xy=(xmax, ymax), xytext=(xmax+1, ymax+1),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "    ax.annotate('Elbow at k:{}'.format(xmax), xy=(xmax, dmax), xytext=(xmax+1, dmax+1),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "    \n",
    "    ax.set_title('K-means Inertia Graph (Elbow method)', fontsize=16)\n",
    "    ax.set_xlabel('Nr of Clusters', fontsize=12)\n",
    "    plt.xticks(ks)\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    print('The best number of clusters is', xmax)\n",
    "    \n",
    "    return xmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best number of clusters is 6\n"
     ]
    }
   ],
   "source": [
    "ideal_clusters = plot_elbow_kmeans(cities_g_avg, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimal number of clusters is not visually very clear, but measuring the distance of each cluster to a line between the fisrt cluster inertia to the last cluster inertia, we can mathematically find the best number of clusters\n",
    "\n",
    "Now I need to profile each cluster (from the best number of clusters found before) to better understand the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>College &amp; University</th>\n",
       "      <th>Food</th>\n",
       "      <th>Nightlife Spot</th>\n",
       "      <th>Outdoors &amp; Recreation</th>\n",
       "      <th>Professional &amp; Other Places</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Travel &amp; Transport</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>PORTO</td>\n",
       "      <td>Aldoar, Foz do Douro e Nevogilde</td>\n",
       "      <td>41.166173</td>\n",
       "      <td>-8.678074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>São Silvestre</td>\n",
       "      <td>40.292124</td>\n",
       "      <td>-8.424989</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AVEIRO</td>\n",
       "      <td>São Bernardo</td>\n",
       "      <td>40.617410</td>\n",
       "      <td>-8.624041</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Real, Dume e Semelhe</td>\n",
       "      <td>41.557560</td>\n",
       "      <td>-8.444078</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>Santo António dos Olivais</td>\n",
       "      <td>40.218000</td>\n",
       "      <td>-8.405990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City                      Neighborhood  Neighborhood Latitude  \\\n",
       "70    PORTO  Aldoar, Foz do Douro e Nevogilde              41.166173   \n",
       "43  COIMBRA                     São Silvestre              40.292124   \n",
       "6    AVEIRO                      São Bernardo              40.617410   \n",
       "30    BRAGA              Real, Dume e Semelhe              41.557560   \n",
       "41  COIMBRA         Santo António dos Olivais              40.218000   \n",
       "\n",
       "    Neighborhood Longitude  Arts & Entertainment  College & University  \\\n",
       "70               -8.678074                   0.0                   0.0   \n",
       "43               -8.424989                   0.0                   0.0   \n",
       "6                -8.624041                   0.0                   0.0   \n",
       "30               -8.444078                   0.0                   0.0   \n",
       "41               -8.405990                   0.0                   0.0   \n",
       "\n",
       "        Food  Nightlife Spot  Outdoors & Recreation  \\\n",
       "70  0.333333        0.333333               0.333333   \n",
       "43  0.500000        0.000000               0.500000   \n",
       "6   0.750000        0.000000               0.000000   \n",
       "30  0.875000        0.000000               0.000000   \n",
       "41  0.800000        0.000000               0.100000   \n",
       "\n",
       "    Professional & Other Places  Shop & Service  Travel & Transport  Cluster  \n",
       "70                          0.0           0.000                 0.0        4  \n",
       "43                          0.0           0.000                 0.0        4  \n",
       "6                           0.0           0.250                 0.0        2  \n",
       "30                          0.0           0.125                 0.0        0  \n",
       "41                          0.0           0.100                 0.0        2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run of the KMeans algorithm\n",
    "f_model = KMeans(n_clusters=ideal_clusters, n_jobs=-1, n_init=30, random_state=123).fit(cities_g_avg.iloc[:,4:])\n",
    "\n",
    "# Assign the labels to it's neighborhoods\n",
    "cities_g_avg['Cluster'] = f_model.labels_\n",
    "\n",
    "# Now a sample of the fitted clusters\n",
    "cities_g_avg.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build of a Cluster profiling dataframe\n",
    "cluster_profile = pd.merge(cities_g_avg[['Neighborhood', 'Cluster']],\n",
    "                          cities_dp,\n",
    "                          on='Neighborhood').groupby(by='Cluster').mean().drop(['Neighborhood Latitude', 'Neighborhood Longitude'], axis=1)*100\n",
    "\n",
    "# melt of the cluster profilig dataframe so seaborn can better help us to visualize it\n",
    "cluster_melt = pd.melt(cluster_profile.reset_index(), id_vars='Cluster', value_name='MeanPCT')\n",
    "\n",
    "g = sb.FacetGrid(data=cluster_melt.sort_values(by='MeanPCT',ascending=False), col='Cluster', col_wrap=int(ideal_clusters/2), sharex=False, height=5, aspect=0.8)\n",
    "g.map(sb.barplot, 'variable', 'MeanPCT', palette='Set1')\n",
    "\n",
    "sb.despine(top=True, right=True, left=True)\n",
    "\n",
    "for ax in g.axes.flat:\n",
    "    for label in ax.get_yticklabels():\n",
    "        label.set_rotation(90)\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_rotation(90)\n",
    "\n",
    "g.fig.subplots_adjust(wspace=.02, hspace=0.65)\n",
    "plt.tight_layout\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.2 Cluster Analysis\n",
    "The classification of these clusters is very subjective, as I've decided to group venues by their parent category and not their current category. but we can still see diferent patterns along the clusters.\n",
    "\n",
    "But this is not the point. Our function is to provide the best neighborhoods to our startup based on what it's important to its founders. This will be the following step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target clusters based on the categories ['Arts & Entertainment', 'Outdoors & Recreation', 'Professional & Other Places', 'Shop & Service', 'Travel & Transport'], are: [2])\n"
     ]
    }
   ],
   "source": [
    "# lets take into account the following categories as the most important to the startup.\n",
    "important_categories = ['Arts & Entertainment', \n",
    "                        'Outdoors & Recreation', \n",
    "                        'Professional & Other Places', \n",
    "                        'Shop & Service',\n",
    "                        'Travel & Transport']\n",
    "\n",
    "# selection of only the clusters that have important categories\n",
    "target_clusters = cluster_profile.apply(lambda x: np.where(x==0, np.nan, x))[important_categories].dropna().index.to_list()\n",
    "print('The target clusters based on the categories {0}, are: {1})'.format(important_categories, target_clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Nr_Neighborhood</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>AVEIRO</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRAGA</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COIMBRA</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LISBOA</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PORTO</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Nr_Neighborhood\n",
       "Cluster City                    \n",
       "2       AVEIRO                 3\n",
       "        BRAGA                  7\n",
       "        COIMBRA                3\n",
       "        LISBOA                19\n",
       "        PORTO                  5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_g_avg_kmeans = cities_g_avg[cities_g_avg['Cluster'].isin(target_clusters)]\n",
    "pd.DataFrame(c_g_avg_kmeans.groupby(by=['Cluster', 'City'])['Neighborhood'].count()).rename(columns={'Neighborhood':'Nr_Neighborhood'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition ot a centroid to help the plot of selected neighborhoods in a folium map\n",
    "cent_lat = c_g_avg_kmeans['Neighborhood Latitude'].sum()/len(c_g_avg_kmeans['Neighborhood Latitude'])\n",
    "cent_long = c_g_avg_kmeans['Neighborhood Longitude'].sum()/len(c_g_avg_kmeans['Neighborhood Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definition of the function to plot the neighborhoods\n",
    "def plot_city_poi_clusters(latitude, longitude, df, z=9):\n",
    "    map_poi = folium.Map(location=[latitude, longitude], zoom_start=z)\n",
    "        \n",
    "    # constrution of a color code for each cluster\n",
    "    colors = ['red', 'blue', 'green', 'purple', 'orange', 'darkred',\n",
    "             'lightred', 'beige', 'darkblue', 'darkgreen', 'cadetblue',\n",
    "             'darkpurple', 'white', 'pink', 'lightblue', 'lightgreen',\n",
    "             'gray', 'black', 'lightgray']\n",
    "    a = np.sort(df['Cluster'].unique()).tolist()\n",
    "    b = colors[:len(a)]\n",
    "    color_code = dict(zip(a,b))\n",
    "    \n",
    "    # adding points to map\n",
    "    for lat, lon, poi, cluster in zip(df['Neighborhood Latitude'],\n",
    "                                      df['Neighborhood Longitude'], \n",
    "                                      df['Neighborhood'], \n",
    "                                      df['Cluster']):\n",
    "        label = folium.Popup(str(poi) + ' Cluster: ' + str(cluster), max_width=300,min_width=100, parse_html=True)\n",
    "        folium.CircleMarker(\n",
    "            [lat, lon],\n",
    "            radius=5,\n",
    "            popup=label,\n",
    "            color=color_code.get(cluster),\n",
    "            fill=True,\n",
    "            fill_color=color_code.get(cluster),\n",
    "            fill_opacity=0.7).add_to(map_poi)\n",
    "        \n",
    "    return map_poi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
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            var html_fb9fcb2c31914ab5817370a1322b4cb4 = $(`<div id="html_fb9fcb2c31914ab5817370a1322b4cb4" style="width: 100.0%; height: 100.0%;">Sé Nova, Santa Cruz, Almedina e São Bartolomeu Cluster: 2</div>`)[0];
            popup_29a0020b27c043b1a07aedadffe3e7df.setContent(html_fb9fcb2c31914ab5817370a1322b4cb4);
        

        circle_marker_e1203c1cfa3d40a8943d3f4da0a6984c.bindPopup(popup_29a0020b27c043b1a07aedadffe3e7df)
        ;

        
    
    
            var circle_marker_185cd1579df74dc9bd16da4498002680 = L.circleMarker(
                [38.69974000000008, -9.181179999999927],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_d10249a690c44afd960dd4b61f2399e7 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_f870862ff04c488a911ac4e2d73ed7be = $(`<div id="html_f870862ff04c488a911ac4e2d73ed7be" style="width: 100.0%; height: 100.0%;">Ajuda Cluster: 2</div>`)[0];
            popup_d10249a690c44afd960dd4b61f2399e7.setContent(html_f870862ff04c488a911ac4e2d73ed7be);
        

        circle_marker_185cd1579df74dc9bd16da4498002680.bindPopup(popup_d10249a690c44afd960dd4b61f2399e7)
        ;

        
    
    
            var circle_marker_cec4c2c5f3c441c2bc795d68e8070891 = L.circleMarker(
                [38.705055000000065, -9.180970999999973],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_10ba79f2babc46bb80fdecdc4fa40053 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_c5cb0f35e0e7412ea9697da7c2712b6a = $(`<div id="html_c5cb0f35e0e7412ea9697da7c2712b6a" style="width: 100.0%; height: 100.0%;">Alcântara Cluster: 2</div>`)[0];
            popup_10ba79f2babc46bb80fdecdc4fa40053.setContent(html_c5cb0f35e0e7412ea9697da7c2712b6a);
        

        circle_marker_cec4c2c5f3c441c2bc795d68e8070891.bindPopup(popup_10ba79f2babc46bb80fdecdc4fa40053)
        ;

        
    
    
            var circle_marker_c193cba35a224699a0b07ff1f578b782 = L.circleMarker(
                [38.75558100000005, -9.137068999999967],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_51d6d35852d740d3a73e584c5f7373ea = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_448f03ec53614f679f822cb97cf8b710 = $(`<div id="html_448f03ec53614f679f822cb97cf8b710" style="width: 100.0%; height: 100.0%;">Alvalade Cluster: 2</div>`)[0];
            popup_51d6d35852d740d3a73e584c5f7373ea.setContent(html_448f03ec53614f679f822cb97cf8b710);
        

        circle_marker_c193cba35a224699a0b07ff1f578b782.bindPopup(popup_51d6d35852d740d3a73e584c5f7373ea)
        ;

        
    
    
            var circle_marker_00d586b3d69a4943a24eb6468213dba4 = L.circleMarker(
                [38.744000000000035, -9.132159999999942],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_81c4d55ec5204acaabb4b76c9fe1e2af = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_7d682a12be5d44d594c9f470bf2d772c = $(`<div id="html_7d682a12be5d44d594c9f470bf2d772c" style="width: 100.0%; height: 100.0%;">Areeiro Cluster: 2</div>`)[0];
            popup_81c4d55ec5204acaabb4b76c9fe1e2af.setContent(html_7d682a12be5d44d594c9f470bf2d772c);
        

        circle_marker_00d586b3d69a4943a24eb6468213dba4.bindPopup(popup_81c4d55ec5204acaabb4b76c9fe1e2af)
        ;

        
    
    
            var circle_marker_1c1bf3d264ad4aa2ad0833312b79cffb = L.circleMarker(
                [38.72238900000005, -9.132703999999961],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_85f66c32438c4077b5ef3697329ffca4 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_c6263f6634b54443af269faa8b54af1d = $(`<div id="html_c6263f6634b54443af269faa8b54af1d" style="width: 100.0%; height: 100.0%;">Arroios Cluster: 2</div>`)[0];
            popup_85f66c32438c4077b5ef3697329ffca4.setContent(html_c6263f6634b54443af269faa8b54af1d);
        

        circle_marker_1c1bf3d264ad4aa2ad0833312b79cffb.bindPopup(popup_85f66c32438c4077b5ef3697329ffca4)
        ;

        
    
    
            var circle_marker_9bdc6e5cfc8e4946b49de75d1aeb5505 = L.circleMarker(
                [38.73024600000005, -9.149493499999947],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_d3205871f2ec4d79af3e3c2a56dce0c0 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_ab1fa257c15940bbbd1bf887ad967de7 = $(`<div id="html_ab1fa257c15940bbbd1bf887ad967de7" style="width: 100.0%; height: 100.0%;">Avenidas Novas Cluster: 2</div>`)[0];
            popup_d3205871f2ec4d79af3e3c2a56dce0c0.setContent(html_ab1fa257c15940bbbd1bf887ad967de7);
        

        circle_marker_9bdc6e5cfc8e4946b49de75d1aeb5505.bindPopup(popup_d3205871f2ec4d79af3e3c2a56dce0c0)
        ;

        
    
    
            var circle_marker_7cb1f3c55b1c4d80937c0bc9da401c3c = L.circleMarker(
                [38.72629500000005, -9.111918499999973],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_6e05e1a0bf35420abdb54c14cdf6aa86 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_585bf7d39aba468a90e8caaa67147fff = $(`<div id="html_585bf7d39aba468a90e8caaa67147fff" style="width: 100.0%; height: 100.0%;">Beato Cluster: 2</div>`)[0];
            popup_6e05e1a0bf35420abdb54c14cdf6aa86.setContent(html_585bf7d39aba468a90e8caaa67147fff);
        

        circle_marker_7cb1f3c55b1c4d80937c0bc9da401c3c.bindPopup(popup_6e05e1a0bf35420abdb54c14cdf6aa86)
        ;

        
    
    
            var circle_marker_093922b043344858963cea282b39d3d3 = L.circleMarker(
                [38.697684000000045, -9.204393499999954],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_7a6df7678de449acaf83308630cb01ec = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_1387a17f405e4b1eac4f9ff27a8912c8 = $(`<div id="html_1387a17f405e4b1eac4f9ff27a8912c8" style="width: 100.0%; height: 100.0%;">Belém Cluster: 2</div>`)[0];
            popup_7a6df7678de449acaf83308630cb01ec.setContent(html_1387a17f405e4b1eac4f9ff27a8912c8);
        

        circle_marker_093922b043344858963cea282b39d3d3.bindPopup(popup_7a6df7678de449acaf83308630cb01ec)
        ;

        
    
    
            var circle_marker_9aa2da29c4134b71bb63fff9d4604728 = L.circleMarker(
                [38.71581000000003, -9.166919999999948],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_dad3271b632b4dd097ba9acd12c3a43f = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_4981fe0045db41f9be9d5367c52a374a = $(`<div id="html_4981fe0045db41f9be9d5367c52a374a" style="width: 100.0%; height: 100.0%;">Campo de Ourique Cluster: 2</div>`)[0];
            popup_dad3271b632b4dd097ba9acd12c3a43f.setContent(html_4981fe0045db41f9be9d5367c52a374a);
        

        circle_marker_9aa2da29c4134b71bb63fff9d4604728.bindPopup(popup_dad3271b632b4dd097ba9acd12c3a43f)
        ;

        
    
    
            var circle_marker_1a772352a5a545a28b3045f4117b07b8 = L.circleMarker(
                [38.71006800000005, -9.159096499999947],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_f70c9e435a964d3987ce777534ceb072 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_850d564c7e5749c59c11cf3a1895555c = $(`<div id="html_850d564c7e5749c59c11cf3a1895555c" style="width: 100.0%; height: 100.0%;">Estrela Cluster: 2</div>`)[0];
            popup_f70c9e435a964d3987ce777534ceb072.setContent(html_850d564c7e5749c59c11cf3a1895555c);
        

        circle_marker_1a772352a5a545a28b3045f4117b07b8.bindPopup(popup_f70c9e435a964d3987ce777534ceb072)
        ;

        
    
    
            var circle_marker_d441162bfc3d40509eff6426b359ee4c = L.circleMarker(
                [38.76411000000007, -9.156999999999924],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_06e588945239430ab94957e46a51ac52 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_6ba5fdf732424d1d9b007c726d4b00bf = $(`<div id="html_6ba5fdf732424d1d9b007c726d4b00bf" style="width: 100.0%; height: 100.0%;">Lumiar Cluster: 2</div>`)[0];
            popup_06e588945239430ab94957e46a51ac52.setContent(html_6ba5fdf732424d1d9b007c726d4b00bf);
        

        circle_marker_d441162bfc3d40509eff6426b359ee4c.bindPopup(popup_06e588945239430ab94957e46a51ac52)
        ;

        
    
    
            var circle_marker_276e7a4c743f4d3a80d801dff1e5f331 = L.circleMarker(
                [38.71188042729671, -9.142968071216105],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_78a67117706747468f103e1e76e309d2 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_c44456c536c5447bab01813c85ccf2d6 = $(`<div id="html_c44456c536c5447bab01813c85ccf2d6" style="width: 100.0%; height: 100.0%;">Misericórdia Cluster: 2</div>`)[0];
            popup_78a67117706747468f103e1e76e309d2.setContent(html_c44456c536c5447bab01813c85ccf2d6);
        

        circle_marker_276e7a4c743f4d3a80d801dff1e5f331.bindPopup(popup_78a67117706747468f103e1e76e309d2)
        ;

        
    
    
            var circle_marker_a5143ee49775423cb8c811bba146f56a = L.circleMarker(
                [38.78382000000005, -9.11890453099994],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_5c6926c83d3f4689a98bd595f16b550b = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_96b1c1e64dbc4ca08ba87b49bbb22212 = $(`<div id="html_96b1c1e64dbc4ca08ba87b49bbb22212" style="width: 100.0%; height: 100.0%;">Olivais Cluster: 2</div>`)[0];
            popup_5c6926c83d3f4689a98bd595f16b550b.setContent(html_96b1c1e64dbc4ca08ba87b49bbb22212);
        

        circle_marker_a5143ee49775423cb8c811bba146f56a.bindPopup(popup_5c6926c83d3f4689a98bd595f16b550b)
        ;

        
    
    
            var circle_marker_825253d9d13d433197c7be7de7a2a9f9 = L.circleMarker(
                [38.76833000000005, -9.097789999999977],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_6015e824404e405c8bade237e2a1d72a = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_2eec6efe20d54e478c523ac7cda864a9 = $(`<div id="html_2eec6efe20d54e478c523ac7cda864a9" style="width: 100.0%; height: 100.0%;">Parque das Nações Cluster: 2</div>`)[0];
            popup_6015e824404e405c8bade237e2a1d72a.setContent(html_2eec6efe20d54e478c523ac7cda864a9);
        

        circle_marker_825253d9d13d433197c7be7de7a2a9f9.bindPopup(popup_6015e824404e405c8bade237e2a1d72a)
        ;

        
    
    
            var circle_marker_2e6d9eb1fd2a490bb56ec1eca62e177a = L.circleMarker(
                [38.730210130409546, -9.132728900475016],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_395107cc555b48858002ccd3e578ccfe = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_886fcffb4e354bda832d3f0e2e1f86b9 = $(`<div id="html_886fcffb4e354bda832d3f0e2e1f86b9" style="width: 100.0%; height: 100.0%;">Penha de França Cluster: 2</div>`)[0];
            popup_395107cc555b48858002ccd3e578ccfe.setContent(html_886fcffb4e354bda832d3f0e2e1f86b9);
        

        circle_marker_2e6d9eb1fd2a490bb56ec1eca62e177a.bindPopup(popup_395107cc555b48858002ccd3e578ccfe)
        ;

        
    
    
            var circle_marker_e26f781517c7424d9eea52582a86a897 = L.circleMarker(
                [38.78032995224781, -9.16044999942306],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_89e3853ed1054af599c20e602ea37d12 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_332fbee85a6c402cae4b0d34c4de3140 = $(`<div id="html_332fbee85a6c402cae4b0d34c4de3140" style="width: 100.0%; height: 100.0%;">Santa Clara Cluster: 2</div>`)[0];
            popup_89e3853ed1054af599c20e602ea37d12.setContent(html_332fbee85a6c402cae4b0d34c4de3140);
        

        circle_marker_e26f781517c7424d9eea52582a86a897.bindPopup(popup_89e3853ed1054af599c20e602ea37d12)
        ;

        
    
    
            var circle_marker_564fdd76ea0946269e5cf3de0e555db4 = L.circleMarker(
                [38.70989000000003, -9.133339999999976],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_b710d797e5cd4c07869fb313004fc5f2 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_90dfe4ad9ce441e1a3bd501c1e2828aa = $(`<div id="html_90dfe4ad9ce441e1a3bd501c1e2828aa" style="width: 100.0%; height: 100.0%;">Santa Maria Maior Cluster: 2</div>`)[0];
            popup_b710d797e5cd4c07869fb313004fc5f2.setContent(html_90dfe4ad9ce441e1a3bd501c1e2828aa);
        

        circle_marker_564fdd76ea0946269e5cf3de0e555db4.bindPopup(popup_b710d797e5cd4c07869fb313004fc5f2)
        ;

        
    
    
            var circle_marker_f77cbaac4e4b47fba09a6e8d8fb311ea = L.circleMarker(
                [38.75360994892959, -9.143020003895094],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_1c7102a1a48b4e8f91986b01c66265db = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_131f42ca7a1a493a9878085d711586eb = $(`<div id="html_131f42ca7a1a493a9878085d711586eb" style="width: 100.0%; height: 100.0%;">Santo António Cluster: 2</div>`)[0];
            popup_1c7102a1a48b4e8f91986b01c66265db.setContent(html_131f42ca7a1a493a9878085d711586eb);
        

        circle_marker_f77cbaac4e4b47fba09a6e8d8fb311ea.bindPopup(popup_1c7102a1a48b4e8f91986b01c66265db)
        ;

        
    
    
            var circle_marker_9037d96250404cce97f9b706560ada8b = L.circleMarker(
                [38.72365503033666, -9.12966982265255],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_3fc9042822e74533ad8a7e5fb0d746f4 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_6deef501a7204daca5355950d0a5510d = $(`<div id="html_6deef501a7204daca5355950d0a5510d" style="width: 100.0%; height: 100.0%;">São Vicente Cluster: 2</div>`)[0];
            popup_3fc9042822e74533ad8a7e5fb0d746f4.setContent(html_6deef501a7204daca5355950d0a5510d);
        

        circle_marker_9037d96250404cce97f9b706560ada8b.bindPopup(popup_3fc9042822e74533ad8a7e5fb0d746f4)
        ;

        
    
    
            var circle_marker_9180bbaebe31422998c8f26643bd30ba = L.circleMarker(
                [41.1483662979302, -8.598389364247119],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_b2da9c4fbaa14525af119e23517385bd = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_ee04b7b6086440548a0b92b84c615b08 = $(`<div id="html_ee04b7b6086440548a0b92b84c615b08" style="width: 100.0%; height: 100.0%;">Bonfim Cluster: 2</div>`)[0];
            popup_b2da9c4fbaa14525af119e23517385bd.setContent(html_ee04b7b6086440548a0b92b84c615b08);
        

        circle_marker_9180bbaebe31422998c8f26643bd30ba.bindPopup(popup_b2da9c4fbaa14525af119e23517385bd)
        ;

        
    
    
            var circle_marker_cb58a4228ef74a2f8085c304cd44f68f = L.circleMarker(
                [41.14864514670074, -8.580614818956356],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_f810dda7bd824f3b8040c6fe30f08ae6 = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_b805c363590742a9a50292c9741ef03d = $(`<div id="html_b805c363590742a9a50292c9741ef03d" style="width: 100.0%; height: 100.0%;">Campanhã Cluster: 2</div>`)[0];
            popup_f810dda7bd824f3b8040c6fe30f08ae6.setContent(html_b805c363590742a9a50292c9741ef03d);
        

        circle_marker_cb58a4228ef74a2f8085c304cd44f68f.bindPopup(popup_f810dda7bd824f3b8040c6fe30f08ae6)
        ;

        
    
    
            var circle_marker_90823e8660d74782abe92246626fe3cb = L.circleMarker(
                [41.14638930181553, -8.614368600994384],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_066c99264bae405c8a59aef14bc36e2e = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_88adbb0f2924426fa506526fa2b80b52 = $(`<div id="html_88adbb0f2924426fa506526fa2b80b52" style="width: 100.0%; height: 100.0%;">Cedofeita, Ildefonso, Sé, Miragaia, Nicolau, Vitória Cluster: 2</div>`)[0];
            popup_066c99264bae405c8a59aef14bc36e2e.setContent(html_88adbb0f2924426fa506526fa2b80b52);
        

        circle_marker_90823e8660d74782abe92246626fe3cb.bindPopup(popup_066c99264bae405c8a59aef14bc36e2e)
        ;

        
    
    
            var circle_marker_dad42cdea6ce46e3953947b54a7ce328 = L.circleMarker(
                [41.15427737012426, -8.648752894561198],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_d0b5b5564e4f4095a86854ff441989eb = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_e6f67f057d9144d19022b88d4755ed51 = $(`<div id="html_e6f67f057d9144d19022b88d4755ed51" style="width: 100.0%; height: 100.0%;">Lordelo do Ouro e Massarelos Cluster: 2</div>`)[0];
            popup_d0b5b5564e4f4095a86854ff441989eb.setContent(html_e6f67f057d9144d19022b88d4755ed51);
        

        circle_marker_dad42cdea6ce46e3953947b54a7ce328.bindPopup(popup_d0b5b5564e4f4095a86854ff441989eb)
        ;

        
    
    
            var circle_marker_cc4e13f6e65844bab2efa3c6acd97cba = L.circleMarker(
                [41.17046625913856, -8.603917337757277],
                {"bubblingMouseEvents": true, "color": "red", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "red", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_6f794d79901247f0ba769333c31b150c);
        
    
        var popup_e6d4aa6e18bc4b75b53c9bf904484b0b = L.popup({"maxWidth": 300, "minWidth": 100});

        
            var html_cb34cb6c526a4e6ab24ea9a568659b14 = $(`<div id="html_cb34cb6c526a4e6ab24ea9a568659b14" style="width: 100.0%; height: 100.0%;">Paranhos Cluster: 2</div>`)[0];
            popup_e6d4aa6e18bc4b75b53c9bf904484b0b.setContent(html_cb34cb6c526a4e6ab24ea9a568659b14);
        

        circle_marker_cc4e13f6e65844bab2efa3c6acd97cba.bindPopup(popup_e6d4aa6e18bc4b75b53c9bf904484b0b)
        ;

        
    
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x271ab08e780>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_city_poi_clusters(cent_lat, # latitude\n",
    "              cent_long, # longitude\n",
    "              c_g_avg_kmeans, z=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we found target clusters of neighborhoods, the elbow method didn't show any abrupt change in inercia, also I can't, by this method, rule out possible neighborhoods that might be outliers and influence the overall results. So i'll try another way to identify the most similar clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Sklean OPTICS <a name=\"optics\"></a>\n",
    "With OPTICS, an advanced algorithm that follows the same principles that of DBSCAN, will identify the right number of clusters and also identify any outliers based on every epsilon value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import OPTICS\n",
    "# run of the OPTICS algorithm\n",
    "cluster_OPTICS = OPTICS(n_jobs=-1, algorithm = 'auto')\n",
    "\n",
    "X = cities_g_avg.iloc[:,3:]\n",
    "cluster_OPTICS.fit(X)\n",
    "\n",
    "n_n = pd.Series(np.arange(len(X)), name='order')\n",
    "reachability = pd.Series(cluster_OPTICS.reachability_[cluster_OPTICS.ordering_], name='reachability')\n",
    "labels = pd.Series(cluster_OPTICS.labels_[cluster_OPTICS.ordering_], name='cluster_label').astype('category')\n",
    "reach_data = pd.concat([n_n, reachability, labels], axis=1)\n",
    "marks = ['X']\n",
    "for i in labels.unique():\n",
    "    marks.append('o')\n",
    "\n",
    "# contrution of a dictionary of colors for each cluster\n",
    "colors = ['grey', 'blue', 'green', 'purple', 'orange', 'dark red',\n",
    "             'light red', 'beige', 'darkblue', 'darkgreen', 'cadet blue',\n",
    "             'dark purple', 'off white', 'pink', 'lightblue', 'lightgreen',\n",
    "             'red', 'black', 'light grey']\n",
    "a = np.sort(labels.unique()).tolist()\n",
    "b = colors[:len(a)]\n",
    "color_code = dict(zip(a,b))\n",
    "\n",
    "# Plot of the reachability plot\n",
    "plt.figure(figsize=(10,5))\n",
    "g = sb.scatterplot(data=reach_data, \n",
    "               x='order', \n",
    "               y='reachability', \n",
    "               style='cluster_label', \n",
    "               palette=sb.xkcd_palette(colors[:len(labels.unique())]), \n",
    "               markers=marks, \n",
    "               hue='cluster_label',\n",
    "               legend='full')\n",
    "g.set_ylabel('Reachability (epsilon distance)', fontsize=12)\n",
    "g.set_title('Reachability Plot', fontsize=16)\n",
    "sb.despine(top=True, right=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the reachability plot we can identify nice cluster and the remainder as outliers. Let's look at a sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Arts &amp; Entertainment</th>\n",
       "      <th>College &amp; University</th>\n",
       "      <th>Food</th>\n",
       "      <th>Nightlife Spot</th>\n",
       "      <th>Outdoors &amp; Recreation</th>\n",
       "      <th>Professional &amp; Other Places</th>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <th>Travel &amp; Transport</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Tadim</td>\n",
       "      <td>41.507680</td>\n",
       "      <td>-8.487787</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Misericórdia</td>\n",
       "      <td>38.711880</td>\n",
       "      <td>-9.142968</td>\n",
       "      <td>0.025974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.610390</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.077922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.077922</td>\n",
       "      <td>0.064935</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>PORTO</td>\n",
       "      <td>Cedofeita, Ildefonso, Sé, Miragaia, Nicolau, V...</td>\n",
       "      <td>41.146389</td>\n",
       "      <td>-8.614369</td>\n",
       "      <td>0.029851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.611940</td>\n",
       "      <td>0.179104</td>\n",
       "      <td>0.074627</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.074627</td>\n",
       "      <td>0.029851</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>São Domingos de Benfica</td>\n",
       "      <td>38.743330</td>\n",
       "      <td>-9.183706</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>Santo António dos Olivais</td>\n",
       "      <td>40.218000</td>\n",
       "      <td>-8.405990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City                                       Neighborhood  \\\n",
       "34    BRAGA                                              Tadim   \n",
       "61   LISBOA                                       Misericórdia   \n",
       "73    PORTO  Cedofeita, Ildefonso, Sé, Miragaia, Nicolau, V...   \n",
       "68   LISBOA                            São Domingos de Benfica   \n",
       "41  COIMBRA                          Santo António dos Olivais   \n",
       "\n",
       "    Neighborhood Latitude  Neighborhood Longitude  Arts & Entertainment  \\\n",
       "34              41.507680               -8.487787              0.000000   \n",
       "61              38.711880               -9.142968              0.025974   \n",
       "73              41.146389               -8.614369              0.029851   \n",
       "68              38.743330               -9.183706              0.100000   \n",
       "41              40.218000               -8.405990              0.000000   \n",
       "\n",
       "    College & University      Food  Nightlife Spot  Outdoors & Recreation  \\\n",
       "34                   0.0  0.333333        0.000000               0.000000   \n",
       "61                   0.0  0.610390        0.142857               0.077922   \n",
       "73                   0.0  0.611940        0.179104               0.074627   \n",
       "68                   0.0  0.500000        0.000000               0.100000   \n",
       "41                   0.0  0.800000        0.000000               0.100000   \n",
       "\n",
       "    Professional & Other Places  Shop & Service  Travel & Transport  Cluster  \n",
       "34                          0.0        0.666667            0.000000        0  \n",
       "61                          0.0        0.077922            0.064935        1  \n",
       "73                          0.0        0.074627            0.029851       -1  \n",
       "68                          0.0        0.300000            0.000000        0  \n",
       "41                          0.0        0.100000            0.000000        3  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_g_avg['Cluster'] = cluster_OPTICS.labels_\n",
    "\n",
    "# sample\n",
    "cities_g_avg.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.1 Cluster Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x1080 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build of a Cluster profiling dataframe\n",
    "cluster_profile = pd.merge(cities_g_avg[['Neighborhood', 'Cluster']],\n",
    "                          cities_dp,\n",
    "                          on='Neighborhood').groupby(by='Cluster').mean().drop(['Neighborhood Latitude', 'Neighborhood Longitude'], axis=1)*100\n",
    "\n",
    "# melt of the cluster profilig dataframe so seaborn can better help us to visualize it\n",
    "cluster_melt = pd.melt(cluster_profile.reset_index(), id_vars='Cluster', value_name='MeanPCT')\n",
    "\n",
    "g = sb.FacetGrid(data=cluster_melt.sort_values(by='MeanPCT',ascending=False), col='Cluster', col_wrap=int(ideal_clusters/2), sharex=False, height=5, aspect=0.8)\n",
    "g.map(sb.barplot, 'variable', 'MeanPCT', palette='Set1')\n",
    "\n",
    "sb.despine(top=True, right=True, left=True)\n",
    "\n",
    "for ax in g.axes.flat:\n",
    "    for label in ax.get_yticklabels():\n",
    "        label.set_rotation(90)\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_rotation(90)\n",
    "\n",
    "g.fig.subplots_adjust(wspace=.02, hspace=0.65)\n",
    "plt.tight_layout\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on what we stated at point 4.1.2, our function is to provide the best neighborhoods to our startup based on what it's important to its founders.\n",
    "\n",
    "but we can clearly state that the clusters show us the following:\n",
    "* Cluster nr -1: outliers (will not be used to make any decision)\n",
    "* Clusters that do not fulfill the interests of the sart up company\n",
    "* Target Clusters based on categories of interest to the start up company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target clusters based on the categories ['Arts & Entertainment', 'Outdoors & Recreation', 'Professional & Other Places', 'Shop & Service', 'Travel & Transport'], are: [1, 2])\n"
     ]
    }
   ],
   "source": [
    "# lets take into account the following categories as the most important to the startup.\n",
    "important_categories = ['Arts & Entertainment', \n",
    "                        'Outdoors & Recreation', \n",
    "                        'Professional & Other Places', \n",
    "                        'Shop & Service',\n",
    "                        'Travel & Transport']\n",
    "\n",
    "# selection of only the clusters that have important categories\n",
    "target_clusters = cluster_profile.drop(-1, axis=0).apply(lambda x: np.where(x==0, np.nan, x))[important_categories].dropna().index.to_list()\n",
    "print('The target clusters based on the categories {0}, are: {1})'.format(important_categories, target_clusters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking into account the target clusters chosen before, as the most probable neighborhoods to establish a startup, let's ranks these (to a maximum of rank 3) with the Nomad List dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>City</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>NomadScore</th>\n",
       "      <th>CostPerMonth</th>\n",
       "      <th>Fun</th>\n",
       "      <th>StartupScore</th>\n",
       "      <th>RANK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Beato</td>\n",
       "      <td>38.726295</td>\n",
       "      <td>-9.111918</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Santa Maria Maior</td>\n",
       "      <td>38.709890</td>\n",
       "      <td>-9.133340</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Santa Clara</td>\n",
       "      <td>38.780330</td>\n",
       "      <td>-9.160450</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Penha de França</td>\n",
       "      <td>38.730210</td>\n",
       "      <td>-9.132729</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Parque das Nações</td>\n",
       "      <td>38.768330</td>\n",
       "      <td>-9.097790</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Olivais</td>\n",
       "      <td>38.783820</td>\n",
       "      <td>-9.118905</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Misericórdia</td>\n",
       "      <td>38.711880</td>\n",
       "      <td>-9.142968</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Lumiar</td>\n",
       "      <td>38.764110</td>\n",
       "      <td>-9.157000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Estrela</td>\n",
       "      <td>38.710068</td>\n",
       "      <td>-9.159096</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Campo de Ourique</td>\n",
       "      <td>38.715810</td>\n",
       "      <td>-9.166920</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Belém</td>\n",
       "      <td>38.697684</td>\n",
       "      <td>-9.204393</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>São Vicente</td>\n",
       "      <td>38.723655</td>\n",
       "      <td>-9.129670</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Avenidas Novas</td>\n",
       "      <td>38.730246</td>\n",
       "      <td>-9.149493</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Arroios</td>\n",
       "      <td>38.722389</td>\n",
       "      <td>-9.132704</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Areeiro</td>\n",
       "      <td>38.744000</td>\n",
       "      <td>-9.132160</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Alvalade</td>\n",
       "      <td>38.755581</td>\n",
       "      <td>-9.137069</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Alcântara</td>\n",
       "      <td>38.705055</td>\n",
       "      <td>-9.180971</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Ajuda</td>\n",
       "      <td>38.699740</td>\n",
       "      <td>-9.181180</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LISBOA</td>\n",
       "      <td>Santo António</td>\n",
       "      <td>38.753610</td>\n",
       "      <td>-9.143020</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174854</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Braga</td>\n",
       "      <td>41.554932</td>\n",
       "      <td>-8.420736</td>\n",
       "      <td>2</td>\n",
       "      <td>0.522613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Braga</td>\n",
       "      <td>41.552475</td>\n",
       "      <td>-8.414283</td>\n",
       "      <td>2</td>\n",
       "      <td>0.522613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Braga</td>\n",
       "      <td>41.549397</td>\n",
       "      <td>-8.421888</td>\n",
       "      <td>2</td>\n",
       "      <td>0.522613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BRAGA</td>\n",
       "      <td>Braga</td>\n",
       "      <td>41.545509</td>\n",
       "      <td>-8.421380</td>\n",
       "      <td>2</td>\n",
       "      <td>0.522613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COIMBRA</td>\n",
       "      <td>Sé Nova, Santa Cruz, Almedina e São Bartolomeu</td>\n",
       "      <td>40.208818</td>\n",
       "      <td>-8.429085</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City                                    Neighborhood  \\\n",
       "11   LISBOA                                           Beato   \n",
       "21   LISBOA                               Santa Maria Maior   \n",
       "20   LISBOA                                     Santa Clara   \n",
       "19   LISBOA                                 Penha de França   \n",
       "18   LISBOA                               Parque das Nações   \n",
       "17   LISBOA                                         Olivais   \n",
       "16   LISBOA                                    Misericórdia   \n",
       "15   LISBOA                                          Lumiar   \n",
       "14   LISBOA                                         Estrela   \n",
       "13   LISBOA                                Campo de Ourique   \n",
       "12   LISBOA                                           Belém   \n",
       "23   LISBOA                                     São Vicente   \n",
       "10   LISBOA                                  Avenidas Novas   \n",
       "9    LISBOA                                         Arroios   \n",
       "8    LISBOA                                         Areeiro   \n",
       "7    LISBOA                                        Alvalade   \n",
       "6    LISBOA                                       Alcântara   \n",
       "5    LISBOA                                           Ajuda   \n",
       "22   LISBOA                                   Santo António   \n",
       "3     BRAGA                                           Braga   \n",
       "2     BRAGA                                           Braga   \n",
       "1     BRAGA                                           Braga   \n",
       "0     BRAGA                                           Braga   \n",
       "4   COIMBRA  Sé Nova, Santa Cruz, Almedina e São Bartolomeu   \n",
       "\n",
       "    Neighborhood Latitude  Neighborhood Longitude  Cluster  NomadScore  \\\n",
       "11              38.726295               -9.111918        1    1.000000   \n",
       "21              38.709890               -9.133340        1    1.000000   \n",
       "20              38.780330               -9.160450        1    1.000000   \n",
       "19              38.730210               -9.132729        1    1.000000   \n",
       "18              38.768330               -9.097790        1    1.000000   \n",
       "17              38.783820               -9.118905        1    1.000000   \n",
       "16              38.711880               -9.142968        1    1.000000   \n",
       "15              38.764110               -9.157000        1    1.000000   \n",
       "14              38.710068               -9.159096        1    1.000000   \n",
       "13              38.715810               -9.166920        1    1.000000   \n",
       "12              38.697684               -9.204393        1    1.000000   \n",
       "23              38.723655               -9.129670        1    1.000000   \n",
       "10              38.730246               -9.149493        1    1.000000   \n",
       "9               38.722389               -9.132704        1    1.000000   \n",
       "8               38.744000               -9.132160        1    1.000000   \n",
       "7               38.755581               -9.137069        1    1.000000   \n",
       "6               38.705055               -9.180971        1    1.000000   \n",
       "5               38.699740               -9.181180        1    1.000000   \n",
       "22              38.753610               -9.143020        1    1.000000   \n",
       "3               41.554932               -8.420736        2    0.522613   \n",
       "2               41.552475               -8.414283        2    0.522613   \n",
       "1               41.549397               -8.421888        2    0.522613   \n",
       "0               41.545509               -8.421380        2    0.522613   \n",
       "4               40.208818               -8.429085        2    0.000000   \n",
       "\n",
       "    CostPerMonth   Fun  StartupScore  RANK  \n",
       "11      0.174854  0.75           1.0     1  \n",
       "21      0.174854  0.75           1.0     1  \n",
       "20      0.174854  0.75           1.0     1  \n",
       "19      0.174854  0.75           1.0     1  \n",
       "18      0.174854  0.75           1.0     1  \n",
       "17      0.174854  0.75           1.0     1  \n",
       "16      0.174854  0.75           1.0     1  \n",
       "15      0.174854  0.75           1.0     1  \n",
       "14      0.174854  0.75           1.0     1  \n",
       "13      0.174854  0.75           1.0     1  \n",
       "12      0.174854  0.75           1.0     1  \n",
       "23      0.174854  0.75           1.0     1  \n",
       "10      0.174854  0.75           1.0     1  \n",
       "9       0.174854  0.75           1.0     1  \n",
       "8       0.174854  0.75           1.0     1  \n",
       "7       0.174854  0.75           1.0     1  \n",
       "6       0.174854  0.75           1.0     1  \n",
       "5       0.174854  0.75           1.0     1  \n",
       "22      0.174854  0.75           1.0     1  \n",
       "3       0.000000  0.00           1.0     2  \n",
       "2       0.000000  0.00           1.0     2  \n",
       "1       0.000000  0.00           1.0     2  \n",
       "0       0.000000  0.00           1.0     2  \n",
       "4       1.000000  0.50           0.0     3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cities_g_avg[cities_g_avg['Cluster'].isin(target_clusters)][['City', 'Neighborhood','Neighborhood Latitude','Neighborhood Longitude','Cluster']]\n",
    "df_NL = pd.merge(df, NL_cities_norm[['City', 'NomadScore', 'CostPerMonth', 'Fun', 'StartupScore']].replace('LISBON', 'LISBOA'), on='City', how='inner')\n",
    "\n",
    "# I'll define a composite score where i sum the nomad score, the fun factor, the startupscore and then substract the costpermonth\n",
    "df_NL['RANK'] = (df_NL['NomadScore']+df_NL['Fun']+df_NL['StartupScore']-df_NL['CostPerMonth']).rank(method='dense', ascending=False).astype(int)\n",
    "df_NL = df_NL.sort_values('RANK', ascending=True)\n",
    "df_NL_final = df_NL[df_NL['RANK']<=3]\n",
    "df_NL_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the centroid coordinates so we can plot the Neighborhoods selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cent_lat = df_NL_final['Neighborhood Latitude'].sum()/len(df_NL_final['Neighborhood Latitude'])\n",
    "cent_long = df_NL_final['Neighborhood Longitude'].sum()/len(df_NL_final['Neighborhood Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definition of the function to plot the neighborhoods\n",
    "def plot_city_poi_clusters_final(latitude, longitude, df, z=9):\n",
    "    map_ranks = folium.Map(location=[latitude, longitude], zoom_start=z)\n",
    "    \n",
    "    # constrution of a dictionary for each cluster\n",
    "    colors = ['red', 'blue', 'green', 'purple', 'orange', 'darkred',\n",
    "             'lightred', 'beige', 'darkblue', 'darkgreen', 'cadetblue',\n",
    "             'darkpurple', 'white', 'pink', 'lightblue', 'lightgreen',\n",
    "             'gray', 'black', 'lightgray']\n",
    "    a = np.sort(df['Cluster'].unique()).tolist()\n",
    "    b = colors[:len(a)]\n",
    "    color_code = dict(zip(a,b))\n",
    "    \n",
    "    # add points to map\n",
    "    for lat, lon, poi, cluster, r in zip(df['Neighborhood Latitude'], \n",
    "                                         df['Neighborhood Longitude'],\n",
    "                                         df['Neighborhood'], \n",
    "                                         df['Cluster'], \n",
    "                                         df['RANK']):\n",
    "        label = folium.Popup(str(poi) + ' Cluster: ' + str(cluster) + ' \\(Rank: ' + str(r) + '\\)', max_width=300,min_width=100, parse_html=True)\n",
    "        folium.CircleMarker(\n",
    "            [lat, lon],\n",
    "            radius=5,\n",
    "            popup=label,\n",
    "            color=color_code.get(cluster),\n",
    "            fill=True,\n",
    "            fill_color=color_code.get(cluster),\n",
    "            fill_opacity=0.6).add_to(map_ranks)\n",
    "\n",
    "    return map_ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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   "source": [
    "plot_city_poi_clusters_final(cent_lat, # latitude\n",
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    "## 5. Results and Discussion <a name=\"discussion\"></a>\n",
    "The results obtained by the exploration of the datasets retrieved and the methedology are very peculiar. First, although not presented in this notebook, the time of the day and day of week change the amount and the categories retrieved by the Foursquare Explore Venues API. This fact leads me to advise a workday and during the day retrieval of the Venue's Data.\n",
    "\n",
    "The profile of each city is very different on its venues, one hipotesis I have for further investigation is the fact that smaller cities might have less venues reported to Foursquare, therefore skewing the data to favor the main city (the capital of Portugal - Lisbon). This might not be a surprise if we check the world most popular cities with data on Foursquare.\n",
    "\n",
    "Furthermore, the amount of venue unique categories is huge comparing to its parent categories. This fact leads me to consider the following scenarios:\n",
    "* If we take into account every categorie we might force k-means or other unsuppervised learning technique into the 'Curse of Dimensionality', and identifying dissimilar neighborhoods into the same cluster.\n",
    "* If we take into account only the parent categories, we might get a very good performing unsupervised learning technique but at the cost of loosing information.\n",
    "\n",
    "Without any other data, like the real population for each neighborhood, the price of housing and commerce per square meter, the overall condition of the neighborhood in various dimensions, etc., might render this analisys dull and very broad. We might identify a neighborhood with all the required venues to a startup thrive, but those venues might have low ratings. The ratings for all the venues were not retrived because we need to have an advanced account of Foursquare Developer (paid), so it was far for the scope of this data science project.\n",
    "\n",
    "After the run of K-Means we found clusters that were easy to tell the appart but somewaht difficult to give them a 'name'. This is also a symptom of the lack of information at the neighborhood level. The running of the OPTICS algorithm gave the ability to rule out neighborhoods that are very different form the others, so clustered as outliers. This helps on finding neighborhood that are similar in a realistic way, even with the lack of further information.\n",
    "\n",
    "Finnaly the use of the Nomad List data, which is at the city level (not neighborhood) was an appropriate tool to rank the selected neighborhoods (those which had the venues and characteristics required for the establisment of the startup) in to a small list. This list of neighborhoods would be presented to the clients so they can make the best informed decision. The majority of neighborhoods presented were located at Lisbon, Porto and Aveiro."
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    "## 6. Conclusion <a name=\"conclusion\"></a>\n",
    "The purpose of this project, was to broadly identify neighborhoods for a startup company to establish in Lisbon, Porto, Braga, Aveiro or Coimbra cities of Portugal. In order to make this identification we colected the needs and desires of that startup to match the best neighborhoods with the apropriated venues redtrived from the Foursquare Database. There were two aproaches to the clustering of the neighborhoods and the one being OPTICS because of its capability of identifying outliers. In the end we present a list of candidate neighborhoods to our clients to establish.\n",
    "\n",
    "Altouhg the data is very dinamic, the decission selection the best location will be made by the client taking into consideration additional factors like attractiveness of each location, levels of noise or proximity to major roads, real estate availability and it's prices, social and economic dynamics of every neighborhood. The data retrieved in this project with the previous additional data could be presented as a list of variables from wich the Client could chose the most important, feed it to our OPTICS model, ranked by Nomad List and, finnaly, the presentation of a more refined and short list of the best neighborhood to setle the Startup"
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